Based on the back propagation and radial basis function neural network, and using the tool of Matlab and Lab Windows/CVI, the real-time fault detection algorithms for the start-up and main-stage process of a certain liquid-propellant rocket engine in ground tests are developed in this paper. The algorithms realized were verified with a great deal of historical test-data and also validated in the practical ground tests of the engine. The results show that the algorithms not only can detect the fault of the engine in time and efficiently without false alarm and missing alarm, but also can meet the real-time ability and robustness requirement.