A Synthetic Aperture Radar (SAR) target recognition method based on the multi-scale monogenic features is proposed. To fully exploit the discrimination capability of the multi-scale monogenic features, the Multiset Canonical Correlation Analysis (MCCA) is used to fuse the different types of monogenic features from different scales including local amplitude, local phase, and local orientation, which results in a feature vector containing the internal correlations of each kind of feature. In the classification stage, the Joint Sparse Representation (JSR) is employed to classify the feature vector fused by the three kinds of features, and to further exploit the internal correlations of different types of features. Finally, the target type is decided according to the reconstruction errors from JSR.Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to evaluate the performance of the proposed method, and the results prove the validity of the proposed method.