Abstract: In order to solve the occlusion issue in SAR image target recognition, a new classification method is proposed based on non-negative sparse representation. The difference between L0-norm and L1-norm minimization in solving non-negative sparse representation problem is analyzed, and it is proved that L1-norm regularization method pursuits not only the sparsity of the solution but also the similarity between the input signal and the selected atoms under some conditions, hence it is fit for classification application. The experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that the non-negative sparse representation classification method with L1-norm regularization can achieve much better recognition performance, and it is more robust in the recognition of targets with occlusion compared with the traditional method.