The paper discusses a connectionist implementation of knowledge engineering concepts and concepts related to production systems in particular. Production systems are one of the most used artificial intelligence techniques as well as a widely explored model of cognition. The use of neural networks for building connectionist production systems opens the door for developing production systems with partial match and approximate reasoning. An architecture of a neural production system (NPS) and its third realization—NPS3, designed to facilitate approximate reasoning—are presented in the paper. NPS3 facilitates partial match between facts and rules, variable binding, different conflict resolution strategies and chain inference. Facts are represented in a working memory by so-called certainty degrees. Different inference control parameters are attached to every production rule. Some of them are known neuronal parameters, receiving an engineering meaning here. Others, which have their context in knowledge engineering, have been implemented in a connectionist way. The partial match implemented in NPS3 is demonstrated on the same test production system as used by other authors. The ability of NPS3 for approximate reasoning is illustrated by reasoning over a set of simple diagnostic productions and a set of decision support fuzzy rules.