Spiking Neural Networks and synaptic learning have recently emerged as viable techniques to solve classification problems characterized by high computational efficiency when implemented on low-power neuromorphic hardware. This paper presents the implementation of a Spiking Neural Network endowed with supervised Spike Timing Dependent Plasticity for touch modality classification. Results demonstrates the ability of the network to learn appropriate connectivity patterns for the classification. The proposed network achieves a total accuracy of 88.3% overcoming similar state-of-the-art solutions.