Artificial neural networks (ANNs) are applied to correlate and predict physico-chemical, transport and thermodynamic properties of seawater. Values of these properties are needed in the design, simulation and optimization of processes in which seawater is used, mainly in the mining industry. Density, vapor pressure, boiling temperature elevation, specific heat, viscosity, thermal conductivity, surface tension, osmotic coefficient, enthalpy, entropy and latent heat of vaporization are analyzed. These properties depend on temperature and salt content in the saline solution, so these are the independent variables considered for the training and testing of the ANN. Several network architectures were considered and correlated, and predicted values of these properties were compared with values obtained from the literature. As a measure of the accuracy of the method, the average deviation and the average absolute deviation are evaluated. The ANN model obtained gave lower deviations than other more sophisticated models presented in the literature. The chosen ANN model gave absolute deviations lower than 0.5%, with a few exceptions, but maximum deviations were always below 1.0% for all properties.