Abstract
This study presents an output of the application of a probabilistic method of inference based on Bayes’ rule in the diagnosis of defects formed during hot-dip galvanising process. Bayesian cause-effect network for given group of surface defects and its causes was build. Many factors causing defects was taken into consideration in like: technological parameters, technological nodes and character of cause. The process of creating knowledge representation of the hot-dip galvanising process was disclosed on chosen defect (discontinuity of coating) and two causes (pH fluxing bath and surface contamination) along with a scheme of reasoning in Bayesian network and its implementation in a Norsys Netica packet. The advantages and drawbacks of a probabilistic method of representation of the incomplete and uncertain empirical knowledge were highlighted.