This study investigates the potential of artificial neural networks (ANNs) to recognize, classify and predict patterns of different fracture sets in the top 450 m in crystalline rocks at the . A Asp . o o Hard Rock Laboratory (HRL), Southeastern Sweden. ANNs are computer systems composed of a number of processing elements that are interconnected in a particular topology which is problem dependent. ANNs have the ability to learn from examples using different learning algorithms; these involve incremental adjustment of a set of parameters to minimize the error between the desired output and the actual network output. Six fracture-sets with particular ranges of strike and dip have been distinguished. A series of trials were carried out using backpropagation (BP) neural networks for supervised classification, and the BP networks recognized different fracture sets accurately. Self-organizing neural networks have been used for data clustering analysis with supervised learning algorithms; (competitive learning and learning vector quantization), and unsupervised learning algorithms; (self-organizing maps). The self-organizing networks adapted successfully to different fracture clusters (sets). A set of trials has been carried out to investigate the effect of changing the network's topologies on the performance of the BP networks. Using two hidden layers with tan-sigmoid and linear transfer functions was beneficial for the performance of BP classification. ANNs improved fracture sets classification that was based on Kamb contouring method with constraint on areas between fracture clusters.