A new classification method called KWGSC (kernel weighted group sparse representation classifier) is proposed for pattern recognition. KWGSC integrates both group sparsity and data locality in the kernel feature space rather than in the original feature space. KWGSC can learn more discriminating sparse representation coefficients for classification. The iteratively update solution of the l\-2,p-norm minimization problem for KWGSC is also presented. There are several appealing aspects associated with KWGSC. Firstly, by mapping the data into the kernel feature space, the so-called norm normalization problem that may be encountered when directly applying sparse representation to non-normalized data classification tasks will be naturally alleviated. Secondly, the label of a query sample can be inferred more precisely by using of distance constraints and reconstruction constraints in together. Thirdly, the l\-2,p regularization (where p∈(0,1］) is introduced to adjust the sparsity of collaborative mechanism for better performance. Numeric example shows that KWGSC is able to perfectly classify data with different normalization strategy, while conventional linear representation algorithms fail completely. Comprehensive experiments on widely used public databases also show that KWGSC is a robust discriminative classifier with excellent performance, being outperforming other state-of-the-art approaches.