This paper describes a case study in which multivariate statistical procedures have been developed to assist in the supervision of an industrial fed-batch fermentation process operated by Biochemie in Austria. The procedures have been developed to enhance the monitoring capabilities of the current system by interfacing directly into the present G2 real-time knowledge based supervisory system. While the G2 rule based system is useful for detecting deviations in single variables, it has been found to be unable to detect some of the more subtle deviations caused by the complex interactions between the process variables. Multivariate statistical techniques have been utilised in this study to provide early indications of deviations from nominal batch behaviour. The cause of these deviations can subsequently be determined by interrogating the information produced by these algorithms. Although the multivariate statistical techniques adopted in this paper are not new, their integration within the industrial supervisory system and the on-line application to the industrial fermentation process is novel.