The palm oil industry in Malaysia has witnessed a prolific growth in recent years. For the past few decades, Malaysia has led the world in terms of production and export of palm oil. Therefore, physical properties and thermodynamic facts of palm oil have become one of the predominant parts in related chemical industries. Efforts to obtain physical properties of palm oil have been made in order to ensure the quality of the product. Experimental work requires time and is not economic wise. Predictions via correlation methods and thermodynamic models are not practical because the methods are less accurate and high cost as well. In this study, models of Artificial Neural Network (ANN) are constructed to study the physical properties of major and minor components of palm oil. The network is built in conjunction with the data obtained from literature and several journals. The network utilized a feed-forward structure with back-propagation algorithm. The physical properties estimated include the liquid density of palm oil, the vapor and liquid mass fraction of palm oil, and the vapor and liquid equilibrium of fatty acids. The major components consist of triglyceride and fatty acids while the minor components are carotenoid, tocopherols, and tocotrienols. Estimations of the physical properties of palm oil using ANN gives smaller errors compared to other alternatives. As an overall, the lowest Root-Mean-Square (RMS) error acquired for the physical properties of palm oil is less than 1% (0.01). These error decisions have pointed out the suitability of ANN for this study.