This article proposed a synthetic aperture radar (SAR) target recognition method by decision level fusion of monogenic signal using random weighting. The sparse representation-based classification ( SRC) was employed to classify the multi-scale and multicomponent monogenic representations. For erro vectors, the random weight matrix was designed to perform the fusion, which includes a large volume of random weight vectors. The statistics of the fused reconstruction errors were analyzed to form the decision values, which reflect the correlations between the test sample and different classes. Finally,the target label was decided by comparison of the decision values. Extensive experiments were conducted on the MSTAR dataset to evaluate the proposed method, which was compared with some existing SAR target recognition methods. The results showed that the proposed method could effectively improve the overall performance.