Machinery health management is becoming increasingly important and the diagnosis of failures based on machinery condition has been analyzed in-depth in the last few decades, and is relatively well understood. However, prognostic evaluation of faults in a machine is a harder task that involves predicting impending faults in the system and determining remaining useful life of the machinery. A survey of algorithms, and a detailed description of a hybrid CBM prognostic techniques being investigated for use in ground vehicle systems will be presented. The system incorporates a number of techniques to process and analyze the current condition of a ground vehicle, and to generate a prognosis for each subsystem in the vehicle. The discussion will describe a means of testing, verifying and iteratively improving prognostic capabilities throughout the lifecycle of the platform.