The experimental evaluation of an automatic procedure for sensor fault detection and identification in a real process under closed-loop control is the objective of the present research. The scheme proposed here is very robust to faults in the main sensors of a multiloop control system, thus improving safety and reliability of plant operations. A state variable transformation is carried out in order to derive a model suitable for Recursive Least Squares (RCS) identification valid for all regimes of operation. The fault detection method is based on a moving window statistical analysis of the estimated model parameters. Simultaneously, a state estimation scheme, based on the Extended Kalman Filter (EKF), enables the fault identification, reduces false alarms and provides redundant measurements for alternative control purposes. Experimental runs were carried out in an industrial-scale pilot plant. Despite the large number of uncertainties and nonlinearities in the process, the system exhibited a good performance when faults occurred in the sensors of the control loops.