Smart environments rely on artificial intelligence techniques to make sense of the sensor data and to use the information for recognition and tracking activities. However, many of the techniques that have been developed are designed for simplified situations. In this paper we discuss a more complex situation, namely recognizing activities when they are interweaved in complex and realistic scenarios. This technology is beneficial for monitoring the health of smart environment residents and for correlating activities with parameters such as energy usage. We describe our approach to interleaved activity recognition and evaluate various probabilistic techniques for activity recognition. We validate our algorithm on real sensor data collecting in our smart apartment testbed.