Kohonen's learning vector quantization (LVQ) is an efficient neural network based technique for pattern recognition. The performance of the method depends on proper selection of the learning parameters. Over-training may cause a degradation in recognition rate of the final classifier. In this paper we introduce constrained learning vector quantization (CLVQ). In this method the updated coefficients in each iteration are accepted only if the recognition performance of the classifier after updating is not decreased for the training samples compared with that before updating, a constraint widely used in many prototype editing procedures to simplify and optimize a nearest neighbor classifier (NNC). An efficient computer algorithm is developed to implement this constraint. The method is verified with experimental results. It is shown that CLVQ outperforms and may even require much less training time than LVQ.