The importance of PVT properties, such as the bubblepoint pressure, solution gas-oil ratio and oil formation volume factor, makes their accurate determination necessary for reservoir performance calculations. An enormous amount of PVT data has been collected and correlated over many years for different types of hydrocarbon systems. Almost all of these correlations were developed with linear or nonlinear multiple regression or graphical techniques that may not lead to the highest accuracy. Artificial neural networks, on the other hand, once successfully trained, can be excellent, reliable predictive tools for the determination of crude oil PVT properties. In this study, we present neural-network-based models for the prediction of PVT properties of crude oils from the Middle East. Several neural-network architectures using back-propagation with momentum for error minimization were investigated to obtain the most accurate PVT correlations. The data on which the network was trained contain 498 experimentally obtained data sets of different crude-oil and gas mixtures from the Middle East region. This represents the largest data set ever collected to be used in developing PVT models for Middle East crude oils. The neural-network model is able to predict the bubblepoint pressure and the oil formation volume factor as a function of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the temperature. A detailed comparison between the results predicted by the neural-network models and those predicted by other correlations are presented for these Middle East crude-oil samples.