A novel method to explain nonlinear classification decisions in terms of input variables is introduced. The method is based on Taylor expansions and decomposes the output of a deep neural network in terms of input variables. The resulting deep Taylor decomposition can be applied directly to existing neural networks without retraining. The method is tested on two large-scale neural networks for image classification: BVLC CaffeNet and GoogleNet.