This paper focuses on how to treat uncertainty in health monitoring of hybrid systems by using a model-based method. The Hybrid Particle Petri Nets (HPPN) formalism is defined in the context of health monitoring to model hybrid systems and to generate diagnosers of such systems. The main advantage of this formalism is that it takes into account knowledge-based uncertainty and uncertainty in diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as faults) and is robust to false observations. It also estimates the continuous state of the system by using particle filtering. Finally, HPPN can represent the system degradation that is often dealt with using proba-bilistic tools. A hybrid technique is thus used to group all this knowledge and to deduce the diagnosis results. The approach is demonstrated on a three-tank system. Experimental results are given, illustrating how different kinds of uncertainty are taken into account when using HPPN.