In the present research, the critical pressures of organic compounds were selected as a model case and were predicted using a quantitative structure-property relationship model. The coverage of prediction contains hydrocarbons and non-hydrocarbon organic compounds containing O,S, and N atoms. In total, 802 hydrocarbons and 1144 non-hydrocarbon organic compounds were used to develop a model with the 3D structure of each compound being optimized by quantum mechanical calculations. Furthermore, appropriate descriptors to explain critical pressure effectively were selected by forward selection regression and genetic algorithm. Multi-linear regression and neural networks were used to establish prediction models for the hydrocarbon and non-hydrocarbon organic compounds. The prediction models achieved suffciently high performances, with R-2>0.96. This research also analyzes implications of selected descriptors, and the relationship between the descriptor and critical pressure.