A paper that investigates the potential of recurrent spiking neurons for classification problems. It presents a hybrid approach based on the paradigm of Reservoir Computing. The practical applications based on recurrent spiking neurons are limited due to the lack of learning algorithms. Most of the previous work in the literature has focused on feed forward networks because computation in these networks is comparatively easy to analyse. The details of such networks have been reported in detail in (Haykin, 1999) (Pavlidis et al., 2005) (Bohte et al., 2000). Recently, a strategy proposed by Maass (Maass et al., 2002) and Jaeger (Jaeger, 2001) offers to overcome the burden of recurrent neural networks training.