Fault diagnosis and prognosis (FDP) are important capabilities that can enable autonomous detection and prediction of failures' progress in complex engineering systems. This paper introduces an innovative modular FDPmethod for a hydro-control valve system. The hydro-control valve is a critical part of the space launch vehicle propulsion system, and health monitoring of this hydro-valve is essential to ensure safety and reliability of the spacecraft. In this study, three main failures, i.e., piston leakage, drain blockage, and filter malfunction, in the hydro-control valve system are considered for monitoring and prognosis. The proposed FDP system has three main components including fault detection and diagnosis (FDD) unit, failure parameter estimation unit, and remaining useful life (RUL) estimation unit. A feature selection strategy and a support vector machine technique are together utilized to capture redundancy in multisensor data information and to isolate failures in the FDD unit. Then, a decentralized network of three adaptive neuro-fuzzy inference systems (ANFIS) is developed to estimate the failure parameters. Afterward, the RUL unit is constructed using an adaptive Bayesian algorithm. Finally, a performance measure, called the relative accuracy index, is introduced and applied to evaluate the performance of the proposed health monitoring system. Simulation studies confirm the effective performance of the proposed design methodology.