The data mining and pervasive computing technologies found in smart homes offer unprecedented opportunities for providing context-aware services, including health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to provide these services, smart environment algorithms need to recognize and track activities that people normally perform as part of their daily routines. However, activity recognition has typically involved gathering and labeling large amounts of data in each setting to learn a model for activities in that setting. We hypothesize that generalized models can be learned for common activities that span multiple environment settings and resident types. We describe our approach to learning these models and demonstrate the approach using eleven CASAS datasets collected in seven environments.