Neural networks and expert systems are now beginning to realise their potential in developing intelligent operational decision support systems. While neural networks are able to learn complex nonlinear functional relations between multiple inputs and outputs, there remains the important limitation that knowledge embedded in the neural network is opaque. This image of a black-box technology is a major factor influencing the acceptability of this approach because it does not improve the heuristic understanding of the domain problem. On the other hand, expert systems make use of logic rules to carry out heuristic reasoning. Knowledge used to reach a conclusion is transparent and can be displayed through HOW and WHY explanation facilities which are an integral part of well-founded expert systems. However, a critical problem with this approach is knowledge acquisition. This contribution introduces a fuzzy neural network to extract fuzzy rules automatically from numerical data. By changing fuzzy membership functions, three types of rules can be extracted, i.e. rules with and without fuzzy membership values and neuro-expert systems which overcome the problems described above.