The paper develops further a recently proposed author’s approach for classification of dynamic data series using a class of Recurrent Neural Network (RNN) called Echo state network (ESN). It exploits the Intrinsic Plasticity (IP) tuning of ESN reservoir of neurons to fit their dynamics to the data fed into the reservoir input. A novel approach for ranking of a data base of dynamic data series into groups using the length of the multidimensional vector of reservoir state achieved after consecutive feeding of each time series into the ESN is proposed here. It is tested on eye tracker recordings of human eye movements during visual stimulation and decision making process. The preliminary results demonstrated the ability of the proposed technique to discriminate dynamic data series.