Principal Component Analysis (PCA) is a commonly used approach for process monitoring [1;2]. SPE (Q statistic) and Hotelling's T² statistics [3] are the commonly used metrics for detecting deviations. While they are adequate for steady-state operations, these statistics are prone to Type-I errors (false positives) when applied to transient operations, such as batch processes and startups, shutdowns, grade change operations etc. in continuous processes. This is because the transient operations violate the basic assumption the statistics are built upon, ie: the normal density distribution of the source data.