diff --git a/lib/decisiontree/id3_tree.rb b/lib/decisiontree/id3_tree.rb
index 39e2b70..43926e3 100755
--- a/lib/decisiontree/id3_tree.rb
+++ b/lib/decisiontree/id3_tree.rb
@@ -69,9 +69,14 @@ module DecisionTree
       # return classification if all examples have the same classification
       return data.first.last if data.classification.uniq.size == 1
 
-      # Choose best attribute (1. enumerate all attributes / 2. Pick best attribute)
+      # Choose best attribute:
+      # 1. enumerate all attributes
+      # 2. Pick best attribute
+      # 3. If attributes all score the same, then pick a random one to avoid infinite recursion.
       performance = attributes.collect { |attribute| fitness_for(attribute).call(data, attributes, attribute) }
       max = performance.max { |a,b| a[0] <=> b[0] }
+      min = performance.min { |a,b| a[0] <=> b[0] }
+      max = performance.shuffle.first if max[0] == min[0]
       best = Node.new(attributes[performance.index(max)], max[1], max[0])
       best.threshold = nil if @type == :discrete
       @used.has_key?(best.attribute) ? @used[best.attribute] += [best.threshold] : @used[best.attribute] = [best.threshold]
diff --git a/spec/id3_spec.rb b/spec/id3_spec.rb
index 9e011ce..5eb67ba 100644
--- a/spec/id3_spec.rb
+++ b/spec/id3_spec.rb
@@ -74,4 +74,19 @@ describe describe DecisionTree::ID3Tree do
     Then { tree.predict([2, "blue"]).should == "not angry" }
   end
 
+  describe "infinite recursion case" do
+    Given(:labels) { [:a, :b, :c] }
+    Given(:data) do
+      [
+        ["a1", "b0", "c0", "RED"],
+        ["a1", "b1", "c1", "RED"],
+        ["a1", "b1", "c0", "BLUE"],
+        ["a1", "b0", "c1", "BLUE"]
+      ]
+    end
+    Given(:tree) { DecisionTree::ID3Tree.new(labels, data, "RED", :discrete) }
+    When { tree.train }
+    Then { tree.predict(["a1","b0","c0"]).should == "RED" }
+  end
+
 end