Convolutional Neural Network(CNN) usually adopts a single network for feature extraction. However, the extracted features are not sufficient, which may result in the poor accuracy in image classification. To solve the problem, it is proposed to use two networks for extracting features simultaneously. Then the two networks are cascaded together to obtain the fused features of the two networks, which makes the extracted features more discriminative.The dual-network cascade uses two network branches for feature extraction. One branch is the traditional CNN. The other branch is the traditional CNN plus the residual operation.Before the next dimensional reduction of the feature map, the two different branches are put together.We use the data sets of 101_food and caltech256 to test the networks. The cascaded network is compared with the two separate branches, and the results are favorable.