Multivariate analysis techniques may be useful in statistical process control (SPC) whenever there is more than one process variable. Multivariate control charting is usually helpful when the effect of multiple parameters is not independent or when some parameters are correlated. This article focuses on parameters that correlate when the Pearson correlation coefficient is greater than 0.1. (Ranging from -1 to +1, the Pearson correlation coefficient is a measure of the degree of the linear relationship between two variables.) Applying univariate control charts is possible but inefficient – and can lead to erroneous conclusions. Multivariate methods that jointly monitor these variables is an alternative approach.