For optimum therapeutic response from drug administered to the lungs, it is paramount that the aerosolised drug is able to deposit in the lower airways. The filtering characteristics of the respiratory tract, however, make this a particularly challenging task. Computational tools afford a cost-effective means of studying the problem, and here we report on the development of a rapid and reliable method for predicting the pattern of deposition of polydisperse aerosols within human lungs using artificial neural networks (ANNs). Literature (experimental) data on lung deposition of monodisperse aerosols were used to train a single ANN to allow for simultaneous predictions of regional and total aerosol particle deposition patterns in human lungs. When used in modelling the fate of polydisperse aerosols in human lungs, the trained ANN was found to give highly accurate predictions for all lung regions, and all (pharmaceutically relevant) particle sizes and breathing conditions (with errors typically <0.025%). Further testing of the ANN, using 'unseen' in vitro and in vivo data, gave good agreement of lung dosages. It is thus concluded that the ANN produced can be used to provide highly reliable estimates of particle deposition from polydisperse pharmaceutical aerosols generated from breath-actuated dry powder inhalers, nebulizers and metered dose inhalers with spacers.