We propose a new approach to feature selection for the classification of image data using two-dimensional (2D) wavelet packet bases. To select key features of the image data, the techniques for the dimension reduction are required for which PCA has been most frequently used. However PCA relies on the eigenvalue system, it is not only sensitive to outliers or perturbations but has a tendency to extract only global features. Since the important features for the image data are often characterized by local information such as edges and spikes, PCA does not provide good solutions to such problems. Also eigen value systems usually require high cost in getting the solutions and the complexity of the algorithm is O(n3), where n is the number of variables, or pixels in the original data. In this paper, original image data are transformed into 2D wavelet packet bases and the best discriminant basis is searched to extract relevant features from image data and to discard irrelevant information. In contrast to PCA solutions, properties of wavelets enable the extraction of detail features with global features. Also, the computational complexity of computing the best 2D wavelet packet basis goes down to approximately O(nlog4 n), where n is the number of pixels in the original image data. Experiment results are compared the recognition rates of PCA and our approach to show that the proposed method gives a better results than PCA in most cases.