This paper presents a constraint-based approach for video complex event recognition with probabilistic reasoning for handling uncertainty. The main advantage of constraint-based approaches is the possibility for human expert to model composite events with complex temporal constraints. But the approaches are usually deterministic and do not enable the convenient mechanism of probability reasoning to handle the uncertainty. The first advantage of the proposed approach is the ability to model and recognize a large amount of composite events with complex temporal constraints. The second advantage is that probability theory provides a consistent framework for dealing with uncertain knowledge for a robust and reliable recognition of complex event. This approach is evaluated with 4 real healthcare videos and a public video database ETISEO'06. The results are compared with state of the art method. The comparison shows that the proposed approach improves significantly the process of recognition and characterizes the likelihood of the recognized events.