A lot of studies are nowadays devoted to structural health monitoring, especially inside the aeronautical environment. In particular, focusing the attention on metallic structures, fatigue cracks represent both a design and maintenance issue. The disposal of real time diagnostic technique for the assessment of structural health has led the attention also toward the prognostic assessment of the residual useful life, trying to develop robust prognostic health management systems to assist the operators in scheduling maintenance actions. The work reported inside this paper is about the development of a Bayesian particle filter to be used to refine the posterior probability density functions of both the damage condition and the residual useful life, given a prior knowledge on damage evolution is available from NASGRO material characterization. The prognostic algorithm has been applied to two cases. The former consists in an off-line application, receiving diagnostic inputs retrieved with manual structure scanning for fault identification. The latter is used on-line to filter the input coming from a real-time automatic diagnostic system. A massive usage of FEM simulations is used in order to enhance the algorithm performances.