Activity recognition is a process by which the ongoing observed behavior of an agent is tracked and mapped to a given model, explaining the behavior and accounting for hidden or unobservable state (e.g., goals or beliefs of the observed agents). Various methods for activity recognition exist. A popular family of such methods rely on Hidden Markov Models HMMs and variants for recognition. These models, however, do not account for changes in transition probabilities based on the duration an agent has spent in a given state. This paper investigates Markov models that go beyond existing models, to explicitly model the dependency of transition probabilities on state duration. In particular, we propose the use of Non-stationary Hidden Semi Markov Models (NHSMMs) in activity recognition. We present the NHSMM model, and compare its performance in recognizing normal and abnormal behavior, using synthetic data from an industry simulator. We show that for relatively simple activity recognition tasks, both HSMMs and NHSMMs easily and significantly outperform HMMs. In more complex tasks, the NHSMMs also outperform the HSMMs, and allow signifi- cantly more accurately recognition.