Multivariate statistical process monitoring (MSPM), contribution plots, and parity space fault diagnosis (FD) techniques are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The methods are illustrated by monitoring the critical control points (CCP) and diagnosing causes of abnormal operation of a pilot pasteurization plant. An empirical model of the process is developed by using subspace state space system identification methods and normal process data. The process data collected under the influence of different magnitude and duration of faults in sensors and actuators are used to validate the MSPM and FD techniques. T-2 and squared prediction error (SPEN) charts are used as MSPM charts. A parity space technique for dynamic stochastic systems and dynamic trends in contribution plots of T-2 and SPEN statistics are used for FD. The detection and FD by these techniques show significant improvements over univariate methods.