diff --git a/examples/plot_svm.py b/examples/plot_svm.py
index f4f2decd8b4e6057995c8b2563ed27ac8a89afb3..d2556eb50119cfa364d52318795225d4d3b28c3b 100644
--- a/examples/plot_svm.py
+++ b/examples/plot_svm.py
@@ -10,9 +10,9 @@ from scikits.learn import svm, datasets
 iris = datasets.load('iris')
 X = iris.data[:, :2] # we only take the first two features. We could
                      # avoid this ugly slicing by using a two-dim dataset
-Y = iris.label
+Y = iris.target
 
-h=.05 # step size in the mesh
+h=.02 # step size in the mesh
 
 # we create an instance of SVM and fit out data. We do not scale our
 # data since we want to plot the support vectors
@@ -28,10 +28,11 @@ Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
 
 # Put the result into a color plot
 Z = Z.reshape(xx.shape)
+pl.set_cmap(pl.cm.Paired)
 pl.pcolormesh(xx, yy, Z)
 
 # Plot also the training points
-pl.scatter(X[:,0], X[:,1], c=iris.label)
+pl.scatter(X[:,0], X[:,1], c=Y)
 # and the support vectors
 pl.scatter(clf.support_[:,0], clf.support_[:, 1], marker='+')
 pl.title('3-Class classification using Support Vector Machine. \n' + \