QSAR models are frequently used to investigate and predict the toxicological effects of chemicals. Building QSAR models of the eye irritation potential of cationic surfactants is difficult, as the mechanism of action of these surfactants is still not fully understood. This report describes a data driven QSAR model to predict Maximum Average Scores (MAS in accordance to Draize) for cationic surfactants from the calculated molecular properties Log P, Log CMC and molecular volume, and the surfactant concentration. We demonstrate that a Bayesian Neural Network, a statistical non-linear regression approach that estimates the noise in the modelling data and error bars on the predictions, provided the most robust and accurate representation of the relationship between the MAS score and the molecular properties. The model provides useful probabilistic predictions for the eye irritancy potential of new or untested cationic surfactants with physicochemical properties lying within the parameter space of the model.