© 2019 Old City Publishing, Inc.ReCA is a reservoir computing architecture based on cellular automata in which the inputs pass on a cellular automaton instead of a recurrent neural network reservoir. ReCA has been tested using pathological synthetic sequence tasks (well-known benchmark tasks within the reservoir computing (RC) community), and has been showing promising results that reduce complexity compared with other RC approaches such as echo state networks (ESNs). In this paper, a number of methods for feature extraction from the cellular automata (CA) reservoir are introduced to improve ReCA by reducing its complexity while maintaining accuracy. The proposed method reduces the feature dimension by using a few states from every time step (EACH) in the reservoir and/or using only one side of the CA evolution (HALF) and/or reducing the CA evolution in space (expansion ratio f ). Due to the rich dynamics of the CA reservoir, the three methods of reduction (EACH, HALF and f ) can be used together to reduce the feature dimension by up to 98% in some pathological tasks compared with the state-of-the-art ReCA results.