The Bidimensional Variational Mode Decomposition (BVMD) is applied to Synthetic Aperture Radar (SAR) image feature extraction and target recognition.Multiple components are generated after BVMD of the original SAR image, which can effectively describe the global and local information of the target.In the phase of the decision-making, the Support Vector Machine (SVM) is adopted to classify the original SAR image and its decompositions separately.Afterwards, linear weighted fusion is employed to combine their results.Finally, based on the fused results, the target label of the test sample can be determined.The proposed method is tested based on the MSTAR dataset respectively under standard operating condition, with pitch angle variation, and with noise interference, which are compared with several existing SAR target recognition methods.The experimental results verify the effectiveness of the proposed method.