This paper proposes a synthetic aperture radar (SAR) target recognition method based on image blocking and matching. The test SAR image is blocked into four patches, which are analyzed and matched separately. For each SAR image patch, the monogenic signal is employed to describe its time-frequency distribution and local details thus to construct the feature vector. The sparse representation-based classification (SRC) is used to classify the four monogenic feature vectors and produce the reconstruction error vectors. For the four reconstruction error vectors, a rich set of random weight vectors are used to fuse them and all the results are analyzed in a statistical way. Finally, the decision value is designed to determine the target label. The proposed method is tested on the MSTAR dataset. The results confirm the validity of the proposed method.