Abstract: A pre-emptive maintenance system for performing maintenance and process assurance on run-critical equipment employs an intrinsic health monitor that includes a number of sensors that are used with an operating equipment or machine to generate a set of intrinsic physical signatures that are products of the primary performance characteristics of the operating equipment. These signatures are nonfunctional parametrics that are consistent and reliable indicators of the normal operation of the equipment and statistical norms are set during an initial learning mode using neural processors and the outputs from the sensors. Thereafter, the sensor outputs are monitored and analyzed using the statistical norms to predict the probabilities of future failures of the operating equipment. By using the intrinsic health monitor an advance in the field of equipment maintenance and process assurance is provided in the approach to maintenance being a pre-emptive procedure.