Statistical process monitoring (SPM) is used in food processing industries to improve productivity and product quality. SPM can also provide information to operators on how close a process is to non-compliance to product safety limits, and carry out periodic checks of sensor accuracy at high frequency. Traditional SPM tools such as Shewhart charts are not appropriate for continuous food processes because of autocorrelation in data. Four alternative SPM techniques are presented and applied to high temperature short time (HTST) dairy pasteurization. The study attempted to achieve compliance of the HTST process operation with the recommended Pasteurized Milk Ordinance by providing a margin between the alarm limits of the monitoring chart and the safety limits. Monitoring of residuals and parameter change detection techniques ave used for monitoring processes with autocorrelated variables. Hotelling's T-2 and residuals of canonical variates techniques are used for monitoring multivariable processes.