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In order to solve the natural images problems of widely types, complex instruction and low classification accuracy, this paper proposed a new classification method for natural images based on feature-weighted K-nearest neighbors. By analyzing the impact of different features on natural images classification, using genetic algorithm to get a set of optimal classification weight vector, weighted textural features and color features based on that data. Finally, it used the adaptive feature-weighted K-nearest neighbors to classify the natural images. The experimental results show that in the constraint of demand classification accuracy and low time complexity that the user given, this algorithm can classify the natural images with high speed and high-precision. The adaptive feature-weighted K-nearest neighbors classification method is generally apply for the variety of natural images, and can improve the classification performance effectively for natural images. [ABSTRACT FROM AUTHOR]