Neural networks may overcome difficulties related to noise and uncertainty. Conventionally, a trial-and-error method must be used to find the proper neural network architecture for a given problem when one is using back-propagation algorithms. Also, the conventional method initializes all weights and thresholds to zero in a neural network, likely resulting in a long training time and poor classification accuracy. We propose an idea for constructing neural networks by making use of decision trees and threshold logic. Decision trees are obtained by an ID3 algorithm. Such a tree is represented by a logic expression for construction of a threshold network which forms the basis of the architecture of the desired neural network. The number of layers and the number of nodes in each layer of the neural network are determined. Initial values for weights and thresholds are also determined. Experiments have shown that a neural network constructed in this manner learns fast and performs efficiently.