A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods.