This paper investigates how information gathered during routine system maintenance actions and residing in numerous disparate databases, could become, if appropriately fused, a virtual sensor providing otherwise unobtainable knowledge of the current state of the system. Understanding the past performance of turbine engine components is currently an exhaustive time-intensive manual task infrequently performed only on a few selected high-value components. The laborious nature of this current state of the art severely limits past performance metrics to very few applications beyond research. A large driver for the collection of engine data has been scheduled engine removal, disassembly and non-destructive component inspection to assure safety and reliability of the entire system. The frequency of these inspections is driven by conservative service within each component's safe life design limits. While this safety assurance process is both time-consuming and expensive, the current use of the information generated is only to determine a go/no-go status for each engine component. It is proposed that this data could have significant additional value for quantifying the remaining useful life of the components and in tailoring schedules for future maintenance actions based on a predicted condition. This paper will illustrate how these engine maintenance databases could provide a source of copious, low-cost, usage-relevant data for statistically validating and establishing confidence in the predictive models which are so critical to a prognosis-based system. This paper leverages a unique opportunity made possible by the Air Force for access to several government owned databases containing decades of turbine engine component inspection information. The evaluation of these databases will specifically consider and support material models currently under development in the DARPA Engine System Prognosis (ESP) program but will also identify opportunities for the larger vision of rapid analytical certification and engineering of future system designs, processes, and materials.