A new Bayesian belief network (BBN) model with discretized nodes is proposed for fault detection and identification in a single sensor. The single-sensor model is used as a building block to develop a BBN model for all sensors in the process considered. A new fault detection index, a fault identification index, and a threshold setting procedure for the multisensor model are introduced. The fault detection index exploits the probabilistic information of the multisensor model, which, along with the proposed threshold setting procedure, leads to effective detection of faulty sensors. The fault identification index uses only the probabilistic information of the faulty sensor to determine in a discretized fashion the size of faults that should be analyzed within a moving time window to identify the fault type. Single-sensor model design parameters (prior and conditional probability data) are optimized to achieve maximum effectiveness in detection and identification of sensor faults. The single-sensor model and optimal values of design parameters are used to develop a multisensor BBN model for a polymerization reactor at steady-state conditions. Its capabilities to detect and identify bias, drift, and noise in sensor readings are illustrated for single and simultaneous multiple faults by several case studies.