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Heating, Ventilating and Air Conditioning (HVAC) systems are used to provide adequate comfort to occupants of spaces within buildings. One important aspect of comfort, the thermal sensation, is commonly assessed by computation of the Predicted Mean Vote (PMV) index. Model-based predictive control may be applied to HVAC systems in existing buildings in order to provide a desired degree of thermal comfort and simultaneously achieve significant energy savings. This control strategy may be formulated as a discrete optimisation problem and solved by means of structured search techniques. Finding the optimal solution depends on the ability of computing many PMV values in a small amount of time. As the PMV formulation involves iterative computations consuming variable time, it is crucial to have a method for fast, possibly constant execution time, computation of the PMV index. In this paper it is experimentally shown that an Artificial Neural Network (ANN) can estimate the PMV index with varying degrees of efficiency over the trade-off of accuracy versus computational speed-up.