diff --git a/examples/scales.ipynb b/examples/scales.ipynb
index 831dbd7b..5d46d83b 100644
--- a/examples/scales.ipynb
+++ b/examples/scales.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -11,37 +11,112 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Plots.jl] Switched to backend: qwt\n"
+      "[Plots.jl] Default backend: immerse\n",
+      "[Plots.jl] Switched to backend: winston"
      ]
     }
    ],
    "source": [
     "using Plots\n",
-    "qwt();"
+    "winston();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[Plots.jl] Initializing backend: winston"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING: Unsupported scale type: none\n"
+      "\n",
+      "WARNING: deprecated syntax \"AbstractVecOrMat{T} (\" at /Users/tom/.julia/v0.4/Winston/src/Winston.jl:96.\n",
+      "Use \"AbstractVecOrMat{T}(\" instead.\n",
+      "INFO: Recompiling stale cache file /Users/tom/.julia/lib/v0.4/Cairo.ji for module Cairo.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.Nothing is deprecated, use Void instead.\n",
+      "WARNING: Union(args...) is deprecated, use Union{args...} instead.\n",
+      " in depwarn at deprecated.jl:73\n",
+      " in call at deprecated.jl:50\n",
+      " in include at ./boot.jl:261\n",
+      " in include_from_node1 at ./loading.jl:304\n",
+      " in require at ./loading.jl:243\n",
+      " in include at ./boot.jl:261\n",
+      " in include_from_node1 at ./loading.jl:304\n",
+      " in require at ./loading.jl:243\n",
+      " in backend at /Users/tom/.julia/v0.4/Plots/src/plotter.jl:145\n",
+      " in plot at /Users/tom/.julia/v0.4/Plots/src/plot.jl:46\n",
+      " in include_string at loading.jl:266\n",
+      " in execute_request_0x535c5df2 at /Users/tom/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
+      " in eventloop at /Users/tom/.julia/v0.4/IJulia/src/IJulia.jl:144\n",
+      " in anonymous at task.jl:447\n",
+      "while loading /Users/tom/.julia/v0.4/IniFile/src/IniFile.jl, in expression starting on line 24\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: requiring \"Dates\" in module \"Winston\" did not define a corresponding module.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
+      "WARNING: module Winston should explicitly import * from Base\n",
+      "INFO: Recompiling stale cache file /Users/tom/.julia/lib/v0.4/Gtk.ji for module Gtk.\n",
+      "2015-10-03 17:15:47.582 julia[40563:138376] *** WARNING: Method userSpaceScaleFactor in class NSView is deprecated on 10.7 and later. It should not be used in new applications. Use convertRectToBacking: instead. \n",
+      "WARNING: [a,b] concatenation is deprecated; use [a;b] instead\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "Plot{Plots.QwtPackage() n=1}"
+       "Plot{Plots.WinstonPackage() n=1}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -52,32 +127,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Unsupported scale type: log2\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "Plot{Plots.QwtPackage() n=1}"
+       "Plot{Plots.WinstonPackage() n=1}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plot(1:1000,1:1000, xscale=:id, yscale=:log2)"
+    "plot(1:1000,1:1000, xscale=:log, yscale=:log2)"
    ]
   },
   {
diff --git a/img/supported/Plots.supportGraphArgs.png b/img/supported/Plots.supportGraphArgs.png
index 2643d22d..18e59c58 100644
Binary files a/img/supported/Plots.supportGraphArgs.png and b/img/supported/Plots.supportGraphArgs.png differ
diff --git a/img/supported/Plots.supportGraphScales.png b/img/supported/Plots.supportGraphScales.png
index 08dc2586..0e03ec09 100644
Binary files a/img/supported/Plots.supportGraphScales.png and b/img/supported/Plots.supportGraphScales.png differ
diff --git a/src/backends/winston.jl b/src/backends/winston.jl
index 685edd3f..ba2043e5 100644
--- a/src/backends/winston.jl
+++ b/src/backends/winston.jl
@@ -71,14 +71,14 @@ supportedArgs(::WinstonPackage) = [
     :ylims,
     # :yrightlabel,
     # :yticks,
-    # :xscale,
-    # :yscale,
+    :xscale,
+    :yscale,
   ]
 supportedAxes(::WinstonPackage) = [:auto, :left]
 supportedTypes(::WinstonPackage) = [:none, :line, :path, :sticks, :scatter, :hist, :bar]
 supportedStyles(::WinstonPackage) = [:auto, :solid, :dash, :dot, :dashdot]
 supportedMarkers(::WinstonPackage) = [:none, :auto, :rect, :ellipse, :diamond, :utriangle, :dtriangle, :cross, :xcross, :star1]
-supportedScales(::WinstonPackage) = [:identity]
+supportedScales(::WinstonPackage) = [:identity, :log10]
 subplotSupported(::WinstonPackage) = false
 
 
@@ -221,7 +221,12 @@ end
 
 # ----------------------------------------------------------------
 
-const _winstonNames = Dict(:xlims => :xrange, :ylims => :yrange)
+const _winstonNames = Dict(
+    :xlims => :xrange,
+    :ylims => :yrange,
+    :xscale => :xlog,
+    :yscale => :ylog,
+  )
 
 function updatePlotItems(plt::Plot{WinstonPackage}, d::Dict)
   window, canvas, wplt = getWinstonItems(plt)
@@ -230,6 +235,14 @@ function updatePlotItems(plt::Plot{WinstonPackage}, d::Dict)
       Winston.setattr(wplt, string(get(_winstonNames, k, k)), d[k])
     end
   end
+
+  for k in (:xscale, :yscale)
+    if haskey(d, k)
+      islogscale = d[k] == :log10
+      Winston.setattr(wplt, (k == :xscale ? :xlog : :ylog), islogscale)
+    end
+  end
+
 end