Baysesian Neural Network approach for predicting the temper embrittlement of steam turbine rotor in service was proposed. The FATT50 (the fracture appearance transition temperature) of the rotors was predicted as a function of ratio of the two peak current densities (Ipr / Ip ) tested by electrochemical potentiodynamic reaction method, temperature of electrolyte, J-factor and grain size ( N ). A database was obtained from the test of electrochemical potentiodynamic reaction and Charpy impact. The Bayesian neural network technique was used for modeling of temper embrittlement. The neural network shows a more precise prediction of temper embrittlement of rotor steels than the prediction using multiple linear regression. The training error and verifying error is with the scatter of ±20°C. The results show that, for the temper embrittlement of rotor steels prediction, the prediction model based on Bayesian neural network is feasible and effective.