A fault diagnosis method for chemical processes based on deep residual network (DRN) was proposed, which could automatically extract fault features from a large number of chemical processes operation data. The model adopted the shortcut connections to alleviate the training difficulty in the traditional deep neural network, and adopted the batch normalization (BN) method, which could effectively alleviate the problem of vanishing/exploding gradients. The Tennessee Eastman (TE) process was used as the experimental object to evaluate the diagnostic performance of the proposed method. The proposed method and the previous TE process fault diagnosis method based on traditional deep learning model were compared. Furthermore, the effects of the number of layers, BN technology and residual structure on fault diagnosis rate were studied. Finally, the output of some layers was visualized by the t-distributed stochastic neighbor embedding (t-SNE) method. The results showed that the model achieved an average fault diagnosis rate of 94% and an average false positive rate of 0.30% for 21 working conditions, showing more excellent diagnostic performance. The two-dimensional scatter plot of the output layer showed clear clustering, which indicated that the proposed DRN model can accurately diagnose the faults.