The effects of processing parameters on the thermoforming of polymeric foam sheets are highly nonlinear and fully coupled. The complex interconnection of these dominant processing parameters makes the process design a difficult task. In this study, the optimal processing parameters of polypropylene foam thermoforming are obtained by the use of an artificial neural network. Data from tests carried out on a lab-scale thermoforming machine were used to train an artificial neural network, which serves as an inverse model of the process. The inverse model has the desired product dimensions as inputs and the corresponding processing parameters as outputs. The structure, together with the training methods, of the artificial neural network is also investigated. The feasibility of the proposed method is demonstrated by experimental manufacturing of cups with optimal geometry derived from the finite element method. Except the dimension deviation at one location, which amounts to 17.14%, deviations of the other locations are all below 3.5%. (c) 2005 Society of Plastics Engineers.