The terms Biker, Punk, Hipster, Goth or Surfer often spark visual depictions of individuals with very distinct fashion styles. These visually salient styles can provide insight into the social identity of an individual. However, despite its potential usefulness, little work has been done to automatically classify images of people into social categories. We tackle this problem by analyzing pictures of groups of individuals and creating models to represent them. We capture the features that distinguish each subculture and show promising results for automatic classification. This work gives vision algorithms access to the social identity of an individual and helps improve the quality of socially motivated image search, relevance of advertisements, and recommendations of social groups.