Vehicle prognostics are used to estimate the remaining useful life of components or subsystems, based on measured vehicle parameters. This paper presents an overview of a vehicle prognostic system, including the critical tasks associated with configuring such a system. The end user of a vehicle prognostic system focuses on the reports generated by the system that provide indications of vehicle readiness, condition and remaining useful life. These reports are based on measurements recorded from sensors on the vehicle and analyzed either on the vehicle or remotely by a “back office” information management system; the latter also provides usage severity trends. To implement such a system, an engineer must first define the vehicle components of interest and determine “damage correlates”: the relationship between damage occurring on key component(s) and key vehicle parameters that can be obtained from vehicle “bus data”. These “damage correlates” and the associated analysis methods are combined with component usage severity information such that the remaining useful life of the component is estimated. One of the most complex steps in this process is calculating “damage correlates” and a specific example is shown for a combat vehicle suspension component. A key suspension component was identified based on maintenance, repair and replacement costs. Measurements were undertaken on durability road surfaces, where loads on the suspension component and vehicle data were measured simultaneously. A damage model for the suspension component was developed based on the measured component loads. This was used to identify key vehicle parameters, establish transfer functions and together with a simple damage model, establish “damage correlates”. The key to success is that the vehicle parameters are easily measurable and can be correlated to specific component damage and deterioration. This paper illustrates the challenges and successful implementation of this approach for a suspension component on a combat vehicle.