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Reasoning systems that integrate explicit knowledge with implicit information are essential for high performance decision support in condition-based maintenance and prognostic health management applications. Such reasoning systems must be capable of learning the specific features of each machine during its life cycle. In this paper, a hybrid reasoning approach that is capable of integrating domain knowledge and test and operational data from the machine is described. This approach is illustrated with an industrial gearbox example. In this approach explicit domain knowledge is expressed as a rule-base and used to train a feedforward neural network. The training process results in a parsimonious representation of the explicit knowledge by combining redundant rules. A significant added practical benefit of this process is that it also is able to identify logical inconsistencies in the rule-base. Such inconsistencies …