Structural health monitoring provides sensor data that can monitor fatigue-induced damage in service. This information may in turn be used to improve the characterization of material properties that govern damage growth for the structure beingmonitored. These properties are oftenwidely distributed amongnominally identicalmaterials because of differences in manufacturing processes and due to aging effects. Improved accuracy in damage growth characteristics would allowmore accurate prediction of the remaining useful life of the structural component. In this paper, a probabilistic approach using Bayesian inference is employed to progressively reduce the uncertainty in structure-specific damage growth parameters in spite of noise and bias in sensor measurements. Starting from an initial wide distribution of damage growth parameters that are obtained from coupon tests, the distribution is progressively narrowed using damage growth data between consecutivemeasurements. Detailed discussions on how to construct the likelihood function under the given noise of sensor data and how to update the distribution are presented. The approach is applied to simulated damage growth in fuselage panels due to cycles of pressurization. It is shown that the proposed method rapidly converges to the accurate damage growth parameters when the initial damage size is relatively large: e.g., 20 mm. Fairly accurate damage growth parameters are obtained even with measurement errors of 5mm. Using the identified damage growth parameters, it is shown that the 95% conservative remaining useful life converges to the true remaining useful life from the conservative side. The proposed approach may have the potential of turning aircraft into flying fatigue laboratories.