A probabilistic method to monitor and predict fatigue crack propagation is presented in this work. The technique makes use of sequential Monte Carlo sampling combined with the probability density functions of the model parameters. The technique leads to an adaptive dynamic state-space model for crack evolution able to identify the most probable parameters, enhancing the estimation of the residual life of the system. The lifetime predictor presented here could be implemented in advanced maintenance strategies for critical structures employed in civil, industrial, and aerospace fields. The algorithm is first applied to a simulated crack growth, and then to some experimental crack propagations from laboratory tests on helicopter panels. The applicability within on-line continuous monitoring systems is discussed at the end of the paper.