Wiener model is a nonlinear representation of systems composed by the coupling of a linear system L and a static nonlinearity N in the form L-N. This model can represent real processes which made it popular in the last decades. The problem of identifying the static nonlinearity and linear system is not a trivial task which has attracted a lot of research interest. In a recent work [I2010], a new methodology for the identification of this system has been proposed. The objective of this thesis is to implement this methodology using numerical simulations. To this end, a real-life Wiener process is taken from the literature. The objective of the study is to program the identification technique of [I2010] using MATLAB/SIMULINK. The inputs are generated by subroutine and the outputs are calculated using the numerical model implemented in MATLAB/SIMULINK. Then, using these input/output data, the unknown static nonlinearity and linear process parameters are determined. The effect of noise on the accuracy of the estimates will also be explored.