Structural entropy is a good candidate for characterizing roughness of surfaces as it is sensitive not only to the general shape of the surface, but also to the rate of the high and low surface points. Wavelet analysis of the surface can separate the larger-scale behavior from the fine details, and together with the structural entropy it can define a behavior profile for the surface which is typically slightly different for new and for worn tribological surfaces. Also it is important to know whether the method of the surface scan has influence on the structural entropy’s wavelet analysis profile, as the lower cost images based on silicone replica and optical scanner have less sensitivity than the higher cost contact scan of the prepared real surface parts. An intelligent fuzzy classification scheme is introduced to characterize surfaces according to both their degree of wear and method of the surface measurement. The basis of the classification is the structural entropies of the original and the first wavelet transform of the height scan of the new and worn surfaces.