To act intelligently in dynamic environments, a system must understand the current situation it is involved in at any given time. This requires dealing with temporal context, handling multiple and ambiguous interpretations, and accounting for various sources of uncertainty. In this paper we propose a probabilistic approach to modeling and recognizing situations. We define a situation as a distribution over sequences of states that have some meaningful interpretation. Each situation is characterized by an individual hidden Markov model that describes the corresponding distribution. In particular, we consider typical traffic scenarios and describe how our framework can be used to model and track different situations while they are evolving. The approach was evaluated experimentally in vehicular traffic scenarios using real and simulated data. The results show that our system is able to recognize and track multiple situation instances in parallel and make sensible decisions between competing hypotheses. Additionally, we show that our models can be used for predicting the position of the tracked vehicles.