As the cost/performance Ratio of vision systems improves with time, new classes of applications become feasible. One such area, automotive applications, is currently being investigated. Applications include occupant detection, collision avoidance and lane tracking. Interest in occupant detection has been spurred by federal automotive safety rules in response to injuries and fatalities caused by deployment of occupant-side air bags. In principle, a vision system could control airbag deployment to prevent this type of mishap. Employing vision technology here, however, presents a variety of challenges, which include controlling costs, inability to control illumination, developing and training a reliable classification system and loss of performance due to production variations due to manufacturing tolerances and customer options. This paper describes the measures that have been developed to evaluate the sensitivity of an occupant detection system to these types of variations. Two procedures are described for evaluating how sensitive the classifier is to camera variations. The first procedure is based on classification accuracy while the second evaluates feature differences.