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This paper reports on a theoretical Bayesian modeling development for residual life prediction in the context of condition-based maintenance. At each monitoring point during a components lifetime, the stochastic filter is used to establish a posterior conditional probability density function (PDF) for the residual life. The PDF can then be used in the evaluation of maintenance and replacement decisions. The research documented in this paper extends the modeling principles in accordance with a practical consideration recognized in a number of previous case applications. Many monitoring scenarios provide evidence that the operational components involved may potentially be subject to a number of individual distinct failure modes, rather than a single dominant failure mode as modeled previously. The modeling procedure proposed to handle this scenario is based on the assumption that an individual monitored component will fail according to one of a number of predefined failure modes. Individual stochastic filters are constructed to facilitate the residual life prediction under the influence of each potential failure mode, and the output from each filter is weighted according to the probability that the particular failure mode is the true underlying (unknown) failure mode. The probabilities associated with each failure mode are recursively derived using a Bayesian model, and the condition monitoring information obtained to date. The modeling process is applied to a set of simulated condition monitoring histories incorporating two potential failure modes, and the results are compared with those obtained using a general model with no failure mode assumptions. The results indicate that, when individual failure modes are identifiable using historical data, the modeling process described in the paper could greatly improve residual life prediction capabilities, and prevent the occurrence of costly component failures.