Viscosity of crude oil is a fundamental factor in simulating reservoirs, forecasting production as well as planning thermal enhanced oil recovery methods which make its accurate determination necessary. Experimental determination of reservoir oil viscosity is costly and time consuming. Hence, searching for quick and accurate determination of reservoir oil viscosity is inevitable. The objective of this study is to present a reliable, and predictive model namely, Least-Squares Support Vector Machine (LSSVM) to predict reservoir oil viscosity. To this end, three LSSVM models have been developed for prediction of reservoir oil viscosity in the three regions including, under-saturated, saturated and dead oil. These models have been developed and tested using more than 1000 series of experimental PVT data of Iranian oil reservoirs. These data include oil API gravity, reservoir temperature, solution gas oil ratio, and saturation pressure. The ranges of data used to develop these new models cover almost all Iranian oil reservoirs PVT data and consequently the developed models could be reliable for prediction of other Iranian oil reservoirs viscosities. In-depth comparative studies have been carried out between these new models and the most frequently used oil viscosity correlations for prediction of reservoir oil viscosity. The results show that the developed LSSVM models significantly outperform the existing correlations and provide predictions in acceptable agreement with experimental data. Furthermore, it is shown that the proposed models are capable of simulating the actual physical trend of the oil viscosity with variation of oil API gravity, temperature, and pressure. (C) 2013 Elsevier Ltd. All rights reserved.