Artificial Intelligence (AI) methods are used to build classifiers that give different levels of accuracy and solution explication. The intent of this paper is to provide a way of building a hierarchical classifier composed of several artificial neural networks (ANN's) organised in a tree-like fashion. Such method of construction allows for partition of the original problem into several sub-problems which can be solved with simpler ANN's, and be built quicker than a single ANN. As the sub-problems extracted start to be independent of one another, this paves a way to realise the solutions for the individual sub-problems in a parallel fashion. It is observed that incorrect classifications are not random and can be therefore used to find clusters defining sub-problems.