In the present study, the ability of an artificial neural network model to evaluate the thermal performance of a cooling tower, which used in the heating, ventilating, and air conditioning industries to reject heat to the atmosphere, is examined. The network is trained with the following experimental values: the ratio of the water mass flow rate to air mass flow rate, the inlet water temperature, and the outlet water temperature, and the inlet air wet-bulb temperature are selected as input variables, while the output is the coefficient of performance. It is concluded that a well-trained neural network provides fast, accurate, and consistent results, making it an easy-to use tool for preliminary engineering studies.