It is demonstrated that the ANN (artificial neural network) approach can be applied to problems in artificial intelligence--in particular, to production systems. Among various types of neural networks, the three-layer ring-structured feedback network with three associative memories is considered to suit the problem domain. Characteristics of the production system paradigm are identified, based on which mapping strategies are developed. Two types of representation techniques are studied: local and hierarchical. The local representation can give an O(1) pattern matching time in production systems when an efficient training strategy is used. The hierarchical representation derives features from production systems and constructs a three-dimensional feature space, where a pattern can be uniquely defined by a vector. Simulation results demonstrate that the proposed architecture and mapping strategy can be an efficient solution to the production system paradigm.