This paper presents methods for visualizing the behaviour of an object recognition convolutional neural network. The first method generates a "canonical image" for a given class that the network can recognize. The second generates a saliency map for a given input image and specified class, that illustrates the part of the image (pixels) that influence the most the given class's output probability. This can be used to seed a graphcut segmentation and localize objects of that class in the input image. Finally, a connection between the saliency map method and the work of Zeiler and Fergus on using deconvolutions to visualize deep networks is established.