In this study, I document the steps taken to develop and apply a Bayesian belief network (BBN) model for identifying the probable black bear intertidal habitat quality of Clayoquot Sound, British Columbia. Initial model outputs provide a narrow range of probability values, resulting in three high quality intertidal habitat classes applied to the study area. Day-time, summer observations of bear intertidal utilization improve previous knowledge of bear behaviour and highlight preferred resources and habitat characteristics, along coastal margins. Black bear encounter rates are calculated for the individual and some combinations of the key environmental variables used within the model. Bear encounter rates increase with higher probability class. A revised BBN model is implemented through systematic methods applied to the comparison of the initial model conditional probability tables and black bear encounter rates. This final model improves the discrimination of intertidal habitats resulting in four classes. The range of probability values increases as do the encounter rates with successive higher probability classes. Future recommendations for improvements are presented.