An accurate quantitative structure-property relationship (QSPR) model, based on the atom-type electrotopological state (E-state) indices and artificial neural network (ANN) technique, for prediction of standard net heat of combustion (Delta H(c)(o)) was developed. An extended set of 49 atom-type electrotopological state (E-state) indices that combined together both electronic and topological characteristics of the analyzed molecules were used as molecular structure descriptors for a diverse set of 1496 organic compounds. Both multilinear regression (MLR) and artificial neural network (ANN) were employed in the modeling. The ANN model with the final optimum network architecture of [49-35-1] gave a significant better performance than the MLR model. The squared correlation coefficient R(2) for the ANN model was R(2) = 0.991 for the training set of 1196 compounds. For the test set of 300 compounds, the corresponding statistics was R(2) = 0.992. The results of this study showed that it would be successful to predict Delta H(c)(o) by using the easily calculated atom-type E-state indices, which can provide one more way for predicting the Delta H(c)(o) of organic compounds for engineering based on only their molecular structures. (C) 2009 Elsevier Ltd. All rights reserved.