In the steel industry there is an increasing demand for automatic inspection systems to control the quality of products. Through the economic pressure on the supplier to the industry, the inspection of a few samples from the production lot is insufficient. Especially in the car industry, a complete, reliable, and automatic surface inspection is necessary. The aim of the research project is to develop sophisticated methods for evaluating the surface quality of steel products. This means that irregularities have to be detected reliably. Further, they have to be classified as erroneous or as non-problematic. Due to the fact that an acceptable intensity image cannot be produced with intensity imaging the investigations are restricted to range imaging. The 3-D model of the surface is acquired by means of light sectioning methods. The research comprises two key activities. Firstly, suitable features have to be investigated which represent the characteristics of the range data well. Secondly, the features are to be combined in order to locate the irregularities embedded in the surface data and further to decide between flawed and intact surface segment. A huge variety of different classification algorithms exist. Special attention will be dedicated to Hidden Markov Models represented as Bayesian network. The issue of decision-making by means of probabilistic networks is a very fundamental approach which might be useful for many intelligent systems.