In this study, a new approach for the prediction of density of pure hydrocarbons such as n-pentane, n-octane, n-decane, and toluene has been suggested. The available experimental data in the literature have been selected at high pressure (similar to 500 MPa) and high temperature (similar to 400 degrees C) conditions. The data are analyzed accurately using artificial neural networks and have been compared with different results obtained by various EOSs such as, PC-SAFT, SAFT, Peng-Robinson and SRK equations. The values of "Average Absolute Deviation Percent" for the densities of each material are calculated using artificial neural networks. These are 0.2 for n-pentane, 0.11 for n-octane, 0.66 for n-decane and 0.51 for toluene, which are substantially more accurate than those obtained with various EOSs. Finally, it has been shown that artificial neural network as an applicable and feasible instrument can be proposed to predict the density data for such materials with high accuracy. (C) 2012 Elsevier B.V. All rights reserved.