Based on four physicochemical descriptors (the rigidness descriptor R OM resulted by hydrogenbonding moieties group and/or rings, the chain mobility n, the molecular average polarizability and the net charge of the most negative atom q À Þ derived from the polymers' monomers structure, the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to construct a model for prediction of the glass transition temperature T g of three classes of vinyl polymers, including polystyrenes, polyacrylates and polymethacrylates. The mean absolute error (MAE ¼ 13:68 K), mean absolute percentage error (MAPE ¼ 4:22%) and correlation coe±cient (R 2 ¼ 0:9252) calculated by SVR are superior to those (MAE ¼ 16:74 K; MAPE ¼ 5:30% and R 2 ¼ 0:9059) achieved by S-SAR model, and (MAE ¼ 16:83 K; MAPE ¼ 5:27% and R 2 ¼ 0:9057) achieved by ANN model for the identical training set (124 vinyl polymers), whereas the MAE ¼ 15:09 K; MAPE ¼ 4:82% and R 2 ¼ 0:9253 calculated by SVR are also better than those of MAE ¼ 17:96 K; MAPE ¼ 5:94% and R 2 ¼ 0:8952 achieved by S-SAR, and MAE ¼ 16:603 K, MAPE ¼ 5:4% and R 2 ¼ 0:9120 achieved by ANN for the same 68 test samples. Furthermore, the MAE, MAPE and R 2 for an independent set (10 vinyl polymers) predicted by SVR also reached 14.132 K, 4.25% and 0.9475, respectively. The results strongly support that the comprehensive modeling and prediction ability of SVR model surpass those of S-SAR and ANN models by applying identical training, test and independent samples. It is demonstrated that the established SVR model is more suitable to be used for prediction of the T g values for unknown vinyl polymers possessing similar structure than S-SAR model or ANN model.