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A new image classification method with multiple feature-based classifier (MFC) is proposed in this paper. MFC does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. In the training stage, a confusion table calculated from each local classifier that uses a specific feature vector group is drawn throughout the accuracy of each local classifier and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the confidence level of each local classifier. The proposed MFC algorithm is applied to the problem of image classification on a set of image data. The results demonstrate that the proposed MFC scheme can optimally enhance the classification accuracy of individual classifiers that use specific feature vector group.