given input parameters but are not sufficiently transparent to allow the extraction of linguistic rules that govern the predictions they make. Consequently, they are called 'black-box' tools because they do not give a transparent view of the rules that govern the relationships that make predictions possible. Rough set theory (RST) was introduced by Pawlak (1991) and is a mathematical tool, which deals with vagueness and uncertainty, and is based on set of rules, which are in terms of linguistic variables. Rough sets are of fundamental importance to computational intelligence and cognitive science and are highly applicable to the tasks of machine learning and decision analysis, especially in the analysis of decisions in which there are inconsistencies. As a consequence of the fact that they are rule-based, rough sets are very transparent but they are not as accurate, and most certainly are not universal approximators, as other machine learning tools such as neural networks in their predictions. It can thus be concluded that in machine learning there is always a trade-off between prediction accuracy and transparency. This paper proposes a combined architecture that takes elements from both rough sets and multi-layered perceptron neural networks. It is, therefore, postulated that this architecture will give a balanced view of the data in terms of both the transparency and accuracy they give. Rough sets are based on lower and upper approximations of decision classes (Inuiguchib and Miyajima, 2006) and are often contrasted to compete with fuzzy set theory (FST), but it in fact complements it. One of the advantages of RST is that it does not require a priori knowledge about the data set, and it is for this reason that statistical methods are not 