A synthetic aperture radar (SAR) target recognition method based on image blocking and matching was proposed. The tested SAR image was blocked into four patches, which described the local regions of the target, respectively. For each SAR image patch, the monogenic signal was employed to construct a feature vector, which described its time-frequency distribution and local details. The monogenic signal decomposed the input image from amplitude, phase, and local orientation. Therefore, it could reflect the local variations in the image so providing more reference information for the analysis of target changes under the extended operating conditions. For the 4 feature vectors, the sparse representation-based classification (SRC) was used for classification and produce the corresponding reconstruction error vectors. Accordingly, based on the linear weighting fusion, the random weight matrix was constructed for analysis. For the results from different weight vectors, an effective decision variable was defined based on statistical analysis. By comparison of the decision values of different classes, the target label of the test sample could be decided. The proposed method made sufficient analysis of the uncertainties about the operating conditions during SAR image measurement, an optimal decision was made based on statistical analysis. Experiments were set up and conducted on the MSTAR dataset including one standard operating condition and three extended operating conditions. Compared with several present methods, the results confirmed the validity of the proposed method.