Abstract: The condensed nearest neighbor (CNN) algorithm is a simple non-parametric prototype selection method, but its prototype selection process is susceptible to pattern read sequence, abnormal patterns and so on. To deal with the above problems, a new prototype selection method based on local mean and class global information is proposed. Firstly, the proposed method makes full use of those local means of the k heterogeneous and homogeneous nearest neighbors to each be-learning pattern and the class global information. Secondly, an updating process is introduced to the proposed method. Lastly, updating strategies are adopted in order to realize dynamic update of the prototype set. The proposed method can not only better lessen the influence of the pattern selected sequence and abnormal patterns on prototype selection, but also reduce the scale of the prototype set. The proposed method can achieve a higher compression efficiency that can guarantee the higher classification accuracy synchronously for original data set. Two image recognition data sets and University of California Irvine (UCI) benchmark data sets are selected as experimental data sets. The experiments show that the proposed method based on the classification performance is more effective than the compared algorithms.