A continuing question in neural net research is the size of network needed to solve a particular problem. If training is started with too small a network for the problem no learning can occur. The researcher must then go through a slow process of deciding that no learning is taking place, increasing the size of the network and training again. If a network that is larger than required is used, then processing is slowed, particularly on a conventional von Neumann computer. An approach to this problem is discussed that is based on learning with a net which is larger than the minimum size network required to solve the problem and then pruning the solution network. The result is a small, efficient network that performs as well or better than the original which does not give a complete answer to the question, since the size of the initial network is still largely based on guesswork but it gives a very useful partial answer and sheds some light on the workings of a neural network in the process.