The combustible nature of organic sulfur containing chemicals demands an accurate hazardous knowledge for their safe handling and application in industries and researches. In this work, a quantitative structure-property relationship (QSPR) study was performed to thoroughly investigate such crucial hazardous properties i.e., flash point (FP) and autoignition temperature (AIT) of the organic sulfur chemicals which are comprising a wide range of mercaptans, sulfides/thiophenes, polyfunctional C,H,O,S material classes. Based on multivariate linear regression (MLR) the multivariate model was gained using a robust binary particle swarm optimization (PSO) for the feature selection step, the three molecular descriptors were realized as the most responsible descriptors for the flammability behaviors of such chemicals. Next, a three-layer feed-forward neural network model (ANN model) was utilized. The implemented multivariate linear regression and three-layer feed-forward neural network models were practically able to predict the flammability characteristics of a diverse range organic sulfur containing chemicals with high accuracy. The results for PSO-MLR model illustrated that the squared correlation coefficient (R-2) between predicted and experimental values were 0.9286 and 0.9259 for FP and AIT, respectively. The results for ANN model showed that the squared correlation coefficients (R-2) were 0.9858 and 0.9889 for FP and AIT, respectively. The ANN model of FP and AIT is more accurate than the multivariate model, and the PSO-MLR model is more simple and touchable. (C) 2012 Elsevier Ltd. All rights reserved.