In this paper, a novel idea of distance, Hit-Distance, was firstly introduced to generalize the representational capacity of available prototypes. Novel adaptive nearest neighbor classifiers based on Hit-Distance were then proposed. Experiments were performed on 8 benchmark datasets from the UCI Machine Learning Repository. It was shown that the proposed classifiers performed much better than the classical nearest neighbor classifier (NN) and the nearest feature line method (NFL), the nearest feature plane method (NFP), the nearest neighbor line method (NNL) and the nearest neighbor plane method (NNP).