This work describes the model of random subspace classifier and provides benchmarking results on the ELENA database. The classifier
uses a coarse coding technique to transform the input real vector into the binary vector of high dimensionality. Thus, class
representatives are likely to become linearly separable. Taking into account the training time, recognition time and error
rate the RSC network in many cases surpasses well known classification algorithms.