This study presents a universal neural-network-based model for the prediction of PVT properties of crude oil samples obtained from all over the world. The data, on which the network was trained, contains 5200 experimentally obtained PVT data sets of different crude oil and gas mixtures from all over the world. They were collected from major-producing oil fields in North and South America, the North Sea, Southeast Asia, the Middle East, and Africa. This represents the largest data set ever collected to be used in developing PVT models. An additional 234 PVT data sets were used to investigate the effectiveness of the neural-network models to predict outputs from inputs that were not used during the training process. The neural network model is able to predict the solution gas-oil ratio and the oil formation volume factor as a function of the bubble-point pressure, the gas relative density, the oil specific gravity, and the reservoir temperature. The neural-network models were developed using back-propagation with momentum for error minimization to obtain the most accurate PVT models. A detailed comparison between the results predicted by the neural-network models and those predicted by other correlations are presented for these crude oil samples. This study shows that artificial neural networks, once successfully trained, are excellent reliable predictive tools for estimating crude oil PVT properties better than available correlations. These neural-network PVT models can be easily incorporated into reservoir simulators and production optimization software.