Modelling the cheese ripening process continues to remain a challenge because this process is a complex system. There is still lack of knowledge to understand the interactions taking place at different level of scale during the process. However, knowledge may be gathered from scientific and operational experts' skills. Integrating this knowledge with knowledge extracted from experimental databases may allow a better understanding of the whole ripening process. This study presents an approach adapted from cognitive science to elicit and formalise experts' knowledge about the camembert-type cheese ripening process. Next, the collected data were unified in a mathematical model based on a dynamic Bayesian network. This formalism makes it possible to integrate this heterogeneous data. The established model presents an average adequacy rate of about 85% with experimental data. (C) 2011 Elsevier Ltd. All rights reserved.