Smart physiological sensors embedded in an automobile afford a novel opportunity to capture naturally occurring episodes of driver stress. In a series of ten ninety minute drives on public roads and highways, electrocardiogram, electromyogram, respiration and skin conductance sensors were used to measure autonomic nervous system activation. The signals were digitized in real time and stored on the SmartCar's Pentium class computer. Each drive followed a pre-specified route through fifteen different events, from which four stress level categories were created according to the results of the subject's self-report questionnaires. In total, 545 one-minute segments were classified. A linear discriminant function was used to rank each feature individually based on recognition performance and a sequential forward floating selection (SFFS) algorithm was used to find an optimal set of features for recognizing patterns of driver stress (88.6%). Using multiple features improved performance significantly over the best single feature performance (62.2%).