A novel method of constructing optimized prototypes for nearest-neighbor classifiers is proposed. Based on an effective classification oriented error function containing class classification and class separation components, the corresponding prototype and feature weight up dating rules are derive d. The propose d method consists of several distinguished properties. First, not only prototypes but also feature weights are constructed during the optimization process; Second, Several in stead of one prototype not belonging to the genuine class of input sample x are updated when x is classified incorrectly; Third, it intrinsically distinguishes different learning contribution from training samples, which enables a large amount of learning from constructive samples, and limited learning from outlier ones. Experiments have shown the superiority of this method compared with LV Q2 and other previous work [1].