Synthetic aperture radar (SAR) images provide high-resolution observations for military targets that can be used for target recognition. A SAR target recognition method is proposed based on correlation analysis and joint decision of multi-level bidimensional intrinsic mode functions (BIMFs) extracted by bidimensional variational mode decomposition (BVMD). As an image decomposition algorithm, BVMD represents SAR images by several BIMFs to describe the global and local properties of targets. Furthermore, non-linear correlation information entropy is adopted as a quantitative measure to choose the optimal subset from the multi-level BIMFs, which are assumed to share high correlations. In the classification phase, joint sparse representation is first employed to represent the selected BIMFs and output their corresponding reconstruction error vectors as well as decisions based on the minimum error principle. Afterward, a voting strategy is conducted on their individual decisions to reach a candidate target label. Once the voting decision is judged reliable, the classification process ends and confirms the candidate target label. Otherwise, a linear weighting algorithm is performed on the reconstruction error vectors from different BIMFs, in which the weights are decided by the voting result. Finally, the target label is determined based on the fused reconstruction errors. Experiments are conducted under different situations including the standard operating conditions and extended operating conditions from the moving and stationary target acquisition and recognition dataset. The results of the proposed method are analyzed and compared with other methods to validate its performance.