This paper is to propose a Markov reliability model which includes the effects of permanent fault, intermittent fault, and transient fault for reliability evaluations. We also provide a new neural network and an improved training algorithm to evaluate the reliability of the fault-tolerant systems. The simulation results show that the neuro-based reliability model can converge faster than that of the other methods. The system state equations for the Markov model are a set of first-order linear differential equations. Usually, the system reliability can be evaluated from the combined state solutions. This technique is very complicated and very difficult in the complex fault-tolerant systems. In this paper, we present a Grey Models (GM(1,1), DF-GM(1,1) and ERC-GM(1,1)) to evaluate the reliability of computer system. It can obtain the system reliability more directly and simply than the Markov model. But the data number for grey model that gets minimal error is different in each time step. Therefore, a feedforward neural network is designed on the basis of more accurate prediction for the grey modeling to evaluate the reliability. Finally, the simulation results show that this technique can lead to better accuracy than the Grey Model.