Summary form only given. A novel method called dynamic node creation (DNC) that attacks issues of training large networks and of testing networks with different numbers of hidden layer units is presented. DNC sequentially adds nodes one at a time to the hidden layer(s) of the network until the desired approximation accuracy is achieved. Simulation results for parity, symmetry, binary addition, and the encoder problem are presented. The procedure was capable of finding known minimal topologies in many cases, and was always within three nodes of the minimum. Computational expense for finding the solutions was comparable to training normal backpropagation (BP) networks with the same final topologies. Starting out with fewer nodes than needed to solve the problem actually seems to help find a solution. The method yielded a solution for every problem tried. BP applied to the same large networks with randomized initial weights was unable, after repeated attempts, to replicate some minimum solutions found by DNC.<>