Feature extraction is a crucial step in pattern recognition problems as well as in methods for characterizing the quality of a product surface (Liu, J. Ph.D. thesis, McMaster University, Canada, 2004). In this paper, different types of wavelet transforms, that is, the wavelet packet transform and the discrete wavelet transform, are compared in the feature extraction step for classification of the surface quality of rolled steel sheets (Bharati, M.; et al. Chemom. Intell. Lab. Syst. 2004, 72, 57-71). Using this real-world industrial example, we have experimentally shown that the wavelet packet transform is superior to the discrete wavelet transform in terms of classification performance and Fisher's criterion. We also propose an easy but powerful method to determine the optimal decomposition level. A closer look at the characteristics of the image data reveals that as a result of its equal frequency bandwidth, wavelet packet transform is more suitable for extracting textural features when textural information from different classes of images is not confined within a certain (spatial) frequency region.