In this paper, we propose a discriminative VQ model for speaker recognition. The VQ training algorithm is developed within the framework of gradient descent learning. Unlike the conventional VQ scheme, which considers only the minimum distance to a speaker codebook, the new algorithm takes account of the distances to all competing classes and all codewords in a speaker codebook, and aims directly at minimizing the recognition error. Two sets of speaker recognition experiments based on the conventional learning scheme and the DVQ are conducted respectively on a database of 200 French speakers. We obtained 1.75% performance improvement in speaker recognition and 0.45% in verification over the conventional VQ algorithm.