In this paper, we present a spiking neural network, inspired by the encoding of edges in human first order tactile afferents. The network uses three layers of Leaky Integrate and Fire neurons to distinguish different edge orientations of a bar pressed on the artificial skin of the iCub robot. The architecture is successfully able to discriminate eight different orientations using a structured model of overlapping receptive fields. We demonstrate that the network can learn the appropriate connectivity through unsupervised spike-based learning and that the spatial distribution of sensitive areas within the receptive fields are important in edge orientation discrimination.