An awareness of the likely future behavior of a batch or a fed-batch fermentation process is valuable information that can be exploited to improve product consistency and maximize profitability. For example, by making operational policy changes in a feedforward control sense, improved consistency can be facilitated, while prior knowledge of batch productivity, or the end time, can help determine the downstream processing configuration and upstream process scheduling. In this article, forecasting methods based on multivariate batch statistical data analysis procedures are contrasted with case-based reasoning (CBR). Additionally, the importance of appropriate statistical data prescreening and the choice of a suitable metric for case selection are investigated. Two industrial case studies are considered, a fed-batch pharmaceutical fermentation and a batch beer fermentation process. It is demonstrated that, following appropriate statistical prescreening of the data, in terms of forecasting performance, CBR is comparable to linear projection to latent structures (PLS), for the more straightforward problem, i.e., the batch beer fermentation, while for the more complex case-the pharmaceutical process-CBR exhibits enhanced performance over PLS.