Joint sparse representation (JSR) has attracted massive attention with many successful applications in pattern recognition recently. In this paper, we propose a novel robust multi-view JSR method referred to as Simultaneous Robust Matching Pursuit (SRMP) based on the outlier-resistant M-estimator originating from robust statistics. Because of the complexity of the objective function, we design an efficient optimization algorithm to implement SRMP based on the half-quadratic theory. In addition, we have also extended the proposed method for the problems of multi-view subspace clustering and multi-view pattern classification, respectively. The experimental results corroborate the efficacy and robustness of SRMP for multi-view data recovery, subspace clustering and classification.