This work describes the experimental implementation of an automatic scheme for the on-line detection and identification (FDI) of faults in the sensors of an industrial scare pilot plant reactor under process control, where a pseudo zero-order exothermic chemical reaction is partially simulated. The main goals of this research are to enhance the safety of reactor operations and to demonstrate the potential of FDI for practical industrial applications.The automatic fault detection and identification method proposed here has two main steps : (1) the detection stage, which relies on a sequential statistical analysis of the process parameters that are continuously estimated by means of a general regression software package (GREG) suitable for non-linear models; (2) the identification step, which is based on an Extended Kalman Filter (EKF) to provide values for the state variables estimates. These values are compared to those given by the sensors thus enabling the identification of the faulty sensor. Moreover, this classification procedure ensures that automatic process control can still be carried on even in such a faulty situation.Despite the strong non-linearities and the high number of uncertainties, the proposed strategy exhibited very promising results concerning the detection and identification of the faulty sensors. Furthermore, it enabled a satisfactory controller performance for a reasonable period of time, when any of the sensors was disabled and control actions were solely based on state estimates.