This paper proposes the use of a coupled fault tree analysis (FTA) and artificial neural network (ANN) model to improve the prediction of the potential risk of coal and gas outburst events during the underground mining of thick and deep Chinese coal seams. The model developed has been used to investigate the gas emission characteristics and the geological conditions that exist within the Huaibei coal mining region, Anhui province, China. The coal seams in this region exhibit a high incidence of coal and gas outbursts. An analysis of the results obtained from an initial application of an FTA model, identified eight dominant model parameters related to the gas content or geological conditions of the coal seams, which characterize the potential risk of in situ coal and gas outbursts. The eight dominant model parameters identified by the FTA method were subsequently used as input variables to an ANN model. The results produced by the ANN model were used to develop a qualitative risk index to characterize the potential risk level of occurrence of coal and gas outburst events. Four different potential risk alarm levels were defined: SAFE, POTENTIAL, HIGH and STRONG. Solutions to the prediction model were obtained using a combination of quantitative and qualitative data including the gas content or gas pressure and the geological and geotechnical conditions of coal seams. The application of this combined solution method identified more explicit and accurate model relationships between the in situ geological conditions and the potential risk of coal and gas outbursts. An analysis of the model solutions concluded that the coupled FTA and ANN model may offer a reliable alternative method to forecast the potential risk of coal and gas outbursts. (C) 2010 Elsevier B.V. All rights reserved.