A back - propagation neural network model was used to predict the physicochemi-cal properties of organic compounds. The efficiency of linear regression and principal component analysis for determining the input parameters was demonstrated. The normal boiling points

critical temperatures

critical volumes

critical pressures

vaporization enthalpies and eccentric factor of organic compounds can be predicted quantitatively by neural network. The estimation accuracy is better enough to meet the demand for chemical engineering simulation and design.