Various kinds of k-Nearest Neighbor(KNN)based classification methods are the bases of many wellestablished and high-performance pattern recognition techniques.However,such methods are vulnerable to parameter choice.Essentially,the challenge is to detect the neighborhood of various datasets while ignoring the data characteristics.This article introduces a new supervised classification algorithm,Natural Neighborhood Based Classification Algorithm(NNBCA).Findings indicate that this new algorithm provides a good classification result without artificially selecting the neighborhood parameter.Unlike the original KNN-based method,which needs a prior k,NNBCA predicts different k for different samples.Therefore,NNBCA is able to learn more from flexible neighbor information both in the training and testing stages.Thus,NNBCA provides a better classification result than other methods.