This paper proposes a high performance texture classification method using dominant energy features from wavelet packet decomposition. We decompose the texture images with a family of real orthonormal wavelet bases and compute the energy signatures using the wavelet packet coefficients. Then we select a few of the most dominant energy values as features and employ a Mahalanobis distance classifier to classify a set of distinct natural textures selected from the Brodatz album. In our experiments, the proposed method employed a reduced feature set and involved less computation in classification time while still archiving high accuracy rate (94.8%) for classifying twenty classes of natural texture images.