Inductive generalization means the ability of a neural network to learn a given algorithm using incomplete information about it. A consideration based on the information theory leads to a simple equation connecting characteristics of the network with those of the algorithm to be learned. The main conclusion is that the most efficient generalization is achieved on the networks with minimal complexity sufficient for realization of the algorithm under consideration. The obtained equation is compared with the results of computer simulations for a universal neural network obtained in the present paper as well as by other workers. A good agreement is observed between theoretical predictions for generalization efficacy and results of computer simulations