The complex nature for steelmaking processes makes the classical Statistical Process Control
(SPC) methodologies are optimal when used to monitor and control steam boiler generation used to
supply the required steam for vacuum degassing processes. These processes include a large number
of variables that need to be monitored and controlled, while classical SPC requires a control chart for
each variable. Thus the effect of one variable can be confounded with effects of other correlated
variables. Such a situation can lead to false alarm signals. Univariate control charts are also difficult
to manage and analyze because of the large numbers of control charts of each variable. An
alternative approach is to construct a single multivariate control T2 chart that minimizes the
occurrence of false process alarms as well as monitors the relationships between the variables, and
identifies real process changes not detectable using univariate charts. It is necessary to
simultaneously monitor and control these variables to achieve optimal vacuum degassing process
performance to remove harmfid gases from the molten steel before casting. This represents the main
concern of the presented paper. This paper also studies the application of univariate and multivariate
control charts in the field of steel industry. The performance analysis for each one is studied using
the Average Run Length (ARL). A comparison of the univariate out-of-control signals based on the
multivariate out-of-control signals is also made to illustrate the efficiency of the Hotelling's T'
statistics.