The visual aspect of rough surfaces such as steel surfaces becomes of great importance for the quality of the final product they are dedicated to. In a previous work, we have solved the theoretically complex problem of automatically classifying surfaces according to the quality of their aspect. In this case, the measurements were based on topographical maps obtained through interferometric microscopy. The resulting data were analyzed by an algorithm based on morphological and statistical features extraction from surface topography, factorial analysis, bootstrap over-sampling and Bayesian classification. It was then important to apply this methodology as efficiently as possible to perform an automatic, on-line and continuous inspection of the product. In this paper, we focus on all steps leading to such an on-line application, among which choosing an optical sensor and the corresponding optical configuration adapted to an industrial environment and overcoming all difficulties to go from first laboratory tests to on-line measurements on fast moving product are particularly determining. Finally, results of on-line acquisitions are displayed, that are in good agreement with expected aspect characterization.