This paper introduces a novel approach, noise signature, in fault detection and isolation, based on the use of errors–in–variables(EIV) models. Differently from more common stochastic environments, in these models all variables (inputs and outputs) are assumed as affected by additive and uncorrelated noise. The identification procedures developed for EIV models allow to estimate the covariance matrix of the noise that constitutes, in absence of faults, a signature for the system. In fact the variations in the estimated noise variances in presence of faults lead to effective ways to detect and isolate faults on both sensors and actuators.