IRSN uses the SYLVIA software [1] for fire safety studies in nuclear installations. The SYLVIA two-zone model was developed by IRSN to simulate a full ventilation network, fire scenarios in a highly confined and mechanically ventilated facility, and airborne contamination transfers inside nuclear installations. In order to take into account the different sources of uncertainty coming from initial and boundary conditions as well as from model parameters, the SYLVIA software is associated with the SUNSET statistical software [2]. However, such a use of SYLVIA software has a major drawback: it requires a large number of runs and a significant statistical analysis what is not always compatible with the requirements of safety assessments in terms of deadlines. To overcome this difficulty, IRSN is currently developing expert systems based on SYLVIA databases [3]. This approach allows deriving the most likely diagnosis or prognosis in a very short time, but also deriving a more complex form of reasoning intertwining prognostic and diagnostic inferences. These expert systems are based on the Bayesian Belief Network (BBN) methodology and consist in two steps: first, a large database obtained from SYLVIA runs allows the estimation of Conditional Probability Tables; then, a message passing algorithm is used to exploit dynamically this data base. The illustrating example is based on the study of the behavior of the final level of aerosol filtration in nuclear facilities, in a fire situation. The holding of the final level of filtration is conditioned by the thermal and mechanical stresses experienced by HEPA (High Efficiency Particulate Air) filters. The database of this expert system is built from the results of 10 million calculations performed with the SYLVIA software. This example illustrates how an expert system can be used as a decision support tool for fire safety analysis in the nuclear area. The development of expert systems represents a new generation of calculation tools in the field of probabilistic fire simulation. They contribute to building the enhanced expertise of tomorrow.