This article argues that tourists do not necessarily maximize their utility in selecting a travel mode; rather, their choice behavior is context dependent. Given particular conditions related to their family, the environment, and other aspects of the tourist experience, they are assumed to use different heuristics. The present study elicits these choice heuristics from observed data using a tree induction algorithm based on the chi-squared automatic interaction detection (CHAID) algorithm. The induced decision rules are presented in decision tables because this formalism allows one to verify the exhaustiveness, exclusiveness, and consistency of a set of decision rules. It is shown that the choice of transport mode is a highly complex decision. The results of the analyses indicate that this methodology can be applied successfully to better understand tourist choice behavior. Moreover, the statistical properties of the decision table generated are satisfactory, especially when probabilistic rules are used.