One most significant challenge in batch process monitoring, compared to continuous process monitoring, is handling three-dimensional historical data for batch processes. Conventional batch process monitoring approaches involve unfolding of such historical data into two dimensions. However, this simple unfolding technique is not applicable if batch duration is not constant. For monitoring of uneven length batch processes, function space analysis based principal component analysis (FSPCA) had been proposed earlier. However, this approach has some limitations because PCA is not effective when it comes to fault diagnosis of highly nonlinear systems. In this paper, a new technique called function space correspondence analysis (FSCA) is proposed for monitoring of uneven length batch processes. The proposed FSCA technique is intended to overcome such limitations and to achieve improved diagnostic performance. The improved performance is due to better discriminatory ability of the CA algorithm. Improved diagnostic capabilities of the proposed FSCA technique is demonstrated using fed batch penicillin cultivation process as a case study. The monitoring results demonstrate improved diagnostic performance with FSCA. © IFAC.