This paper treats the identification of linear systems in the presence of nonlinear distortions. It extends the theory developed previously for measurement setups where the input is exactly known and the output is observed with errors (output error framework) to measurement setups where both the input and output are observed with errors (errors-in-variables framework). An appropriate measurement strategy and identification algorithm are presented.