Process faults may be detected on-line using existing measurements based upon modelling that is entirely data driven. A multivariate statistical model is developed and used for fault diagnosis of an industrial fed-batch fermentation process. Data from several (25) batches are used to develop a model for cultivation behaviour. This model is validated against 13 data sets and demonstrated to explain a significant amount of variation in the data. The multivariate model may directly be used for process monitoring. With this method faults are detected in real time and the responsible measurements are directly identified. The fault detection and identification is enabled through inspection of a few simple plots. Thus, the presented methodology allows the process operator to actively monitor data from several cultivations simultaneously.