This work proposes a synthetic aperture radar (SAR) target-recognition algorithm based on bidimensional empirical mode decomposition (BEMD). BEMD can extract multilevel bidimensional intrinsic mode functions (BIMFs) from the original image, which facilitates a more accurate description of target details. Therefore, a combination of the original SAR images and BIMFs can provide more useful information for further classification. Support vector machines (SVMs) are employed to classify the original SAR images and BIMFs. Afterwards, the outputs from all SVMs are fused using Bayesian theory to obtain more robust recognition results. Some typical experimental setups are designed based on the MSTAR dataset to test the performance of the proposed method. The results validate the superiority of the proposed method over several current SAR target-recognition algorithms.