SVM learning strategy based on progressive reduction of the number of training vectors is used for MLP training. Threshold for acceptance of useful vectors for training is dynamically adjusted during learning, leading to a small number of support vectors near decision borders and higher accuracy of the final solutions. Two problems for which neural networks have previously failed to provide good results are presented to illustrate the usefulness of this approach.