Synthetic aperture radar (SAR) target recognition method based on decision fusion of convolutional neural network (CNN) and sparse representation-based classification (SRC) was proposed. CNN learned the multi-level features of SAR images through the deep networks, and then judged the target category to which it belonged. Studies had shown that CNN could achieve good recognition performance with sufficient training samples. However, for the conditions which were not included in the training samples, the classification performance of CNN usually decreased significantly. Therefore, the test samples to be identified by CNN were used for classification, and then the reliability of the current classification results was calculated according to the output decision value (i.e. the posterior probability corresponding to each training category). When the classification result was judged to be reliable, the decision of CNN was directly adopted and the target category of the test sample was output. On the contrary, several candidate categories were screened according to the decision values output by CNN, and then a global dictionary was constructed based on their training samples for SRC. For the classification results of SRC, the Bayesian fusion algorithm was further used to fuse it with the classification results of CNN. Finally, the target category of the test sample was determined based on the fused result. The proposed method integrated the advantages of CNN and SRC through a hierarchical way, which was conducive to taking advantage of them for different test conditions and improving the robustness of recognition. In the experiment, tests and analysis were carried out based on the MSTAR dataset, and the results verified the effectiveness of the proposed method.