In this paper, a class-oriented local structure preserving dictionary learning (CLPDL) algorithm is developed for synthetic aperture radar (SAR) target recognition. Unlike most sparse representation algorithms whose sparse model is predefined via dictionary with atoms being training samples themselves, class-oriented dictionary leaning can derive multiple class-dictionary from training set. To preserve data local structure, a local weighted constraint, i.e., Tikhonov regularization, is introduced into the dictionary learning procedure, which is very helpful for certain challenging scenarios such as configuration recognition and large depression variations. Moreover, to reduce the influence of target aspect sensitivity of SAR image on target recognition, the query sample is represented as a linear combination of class-dictionary to eliminate disturbances. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate the validity of the proposed method.