We propose a quality classification methodology based on optimal textural feature selection. This method employs the wavelet packet transform to decompose the original image into multiple-resolution images. Wavelet texture analysis is applied to extract quality-related features from subimages. Optimal textural feature selection is employed to select the discriminative texture in accordance with class information. The previously used best basis approach is incapable of optimal texture classification when combined with wavelet texture analysis. The proposed texture classification method unifies the best basis approach with wavelet texture analysis. Further, we improve the previous best basis to obtain an optimal basis using a simple rule to select discriminative signatures. The proposed methodology is applied and validated for classifying the surface quality of rolled steel sheets. Experimental results show that features extracted using the proposed method are more discriminative than those obtained using the best basis in terms of classification performance and Fisher's index.