High-molecular-weight esters are frequently used to plasticize commercial polymers. Because the physical properties of the polymer matrix can be significantly modified by the type of the plasticizer that is used, these high-molecular-weight esters have been extensively investigated during the last decade. Liquid density, for example, has been found to be a useful bulk property of these liquid plasticizers, since density helps define the internal molecular flexibility. Most of the time, however, experimental difficulties prevent the accurate determination of density. Moreover, the existing empirical models are tedious because of many ill-defined parameters. In this study a simple artificial neural-network-based model was utilized for the density of these esters as a function of their chemical structure and temperature. The sum of squared error for densities predicted by this proposed model was within 0.0009. In this study, the density of a series of five different esters was investigated. These were DSEs, DDEs, TGEs, TTEs, and PTEs.