AbstractMany finite element models use adjustable parameters that control the heat loss to the backing bar, as well as the heat input to the weld. In this paper, we describe a method for determining these parameters with a hybrid artificial neural network (ANN) coupled thermal flow process model of the friction stir welding process. The method successfully determined temperature dependent boundary condition parameters for a series of friction stir welds in 3·2 mm thick 7449 aluminium alloy. The success of the technique depended on the method used to input thermal data into the ANN and the ANN topology. Using this technique to obtain the adjustable parameters of a model is more efficient than the conventional trial and error approach, especially where complex boundary conditions are implemented.