In the present study, a group contribution model is developed for determination of the vapor pressure of pure chemical compounds at temperatures from 55 to 3040 K. About 42 000 vapor pressure values belonging to around 1400 chemical compounds (mostly organic ones) at different temperatures are treated to propose a reliable and predictive model. A three-layer artificial neural network is optimized using the Levenberg-Marquardt (LM) optimization algorithm to establish the final relationship between the functional groups and the vapor pressure values. The obtained results indicate the average absolute relative deviation (AARD%) of the calculations/estimations from the applied data to be about 6% and a squared correlation coefficient of 0.994. Furthermore, the outliers of the model are detected using the leverage value statistics method.