The aim of the thesis is to investigate the use of a particular class of recurrent neural network (RNN), called RNN-OE, in the context of direct data-driven controller (DDDC) design. We consider the problem of controlling both linear and non-linear systems. The peculiar feature of the DDDC approach is that no information is known about the mathematical model of the plant to be controlled; the only available information are the input and the output data collected by performing an open loop experiment on the plant. The controller is designed in such a way that the closed loop response of the system behaves like a linear model reference, designed to satisfy specific transient and steady-state quantitativerequirements.