Dewpoint pressure (DPP) is one of the most important factors to be evaluated by reservoir engineers while planning the development of a gas condensate reservoir. Below the dewpoint pressure, liquid condenses out of the gaseous phase. This liquid condensate forms a “ring” or “bank” around the producing well in the near-well region. Normally this liquid will not flow until its saturation exceeds the critical condensate saturation (Scc) due to the capillary pressure and relative permeability of the porous medium. Hence it is very essential to accurately predict the dewpoint pressure of the reservoir fluid.Numerous studies have been done on predicting dewpoint pressure using neural networks. All of these studies focus on four key input parameters: Reservoir Temperature, Specific gravity, Compressibility factor and Molecular weight of heavier components (C7+). However, in this study, two multi-layer perception neural networks (MLPNN) were built. In developing the MLPNN models two new input parameters were introduced; Critical pressure and Molecular weight of lighter components (C6-).