In this paper, an atificial neural network model is adopted to study the glass transition temperature of polymers. Inour artificial neural networks, the input nodes are the characteristic ratio C_∞, the average molecular weigh M_e betweenentanglement points and the molecular weigh M_(mon) of repeating unit. The output node is the glass transition temperature T_g,and the number of the hidden layer is 6. We found that the artificial neural network simulations are accurate in predicting theoutcome for polymers for which it is not trained. The maximum relative error for predicting of the glass transitiontemperature is 3.47%, and the overall average error is only 2.27%. Artificial neural networks may provide some new ideas toinvestigate other properties of the polymers. In this paper, an atificial neural network model is taken to study the glass transition temperature of polymers. Inour artificial neural networks, the input nodes are the characteristic ratio C_∞, the average molecular weigh M_e betweenentanglement points and the molecular weigh M_ (mon) of repeating unit. The output node is the glass transition temperature T_g, and the number of the hidden layer is 6. We found that the artificial neural network simulations are accurate in predicting theoutcome for polymers for which it is not trained. The maximum relative error for predicting the glass transitiontemperature is 3.47%, and the overall average error is only 2.27%. Artificial neural networks may provide some new ideas to investigate other properties of the polymers.