This article discusses subgroup identification, the goal of which is to determine the heterogeneity of treatment effects across subpopulations. Searching for differences among subgroups is challenging because it is inherently a multiple testing problem with the complication that test statistics for subgroups are typically highly dependent, making simple multiplicity corrections such as the Bonferroni correction too conservative. In this article, a Bayesian approach to identify subgroup effects is proposed, with a scheme for assigning prior probabilities to possible subgroup effects that accounts for multiplicity and yet allows for (preexperimental) preference to specific subgroups. The analysis utilizes a new Bayesian model selection methodology and, as a by-product, produces individual probabilities of treatment effect that could be of use in personalized medicine. The analysis is illustrated on an example involving subgroup analysis of biomarker effects on treatments.