Online batch process monitoring has been a challenging task, as batch processes do not operate around a nominal steady state operating point. Various monitoring approaches where future batch trajectory is filled with average (nominal) batch trajectory have been proposed. Predicting future trajectory for a batch process is a difficult task. Recently a multiway principal component analysis (MPCA) based approach that does not involve future trajectory prediction was proposed. In this paper a new technique based on function space principal component analysis (FSPCA) is proposed for online batch process monitoring. The main advantage of the proposed FSPCA based methodology is its ability to detect incipient and small to medium magnitude faults and its relevance for uneven length batch processes. Efficiency and effectiveness of the proposed algorithm is demonstrated via a fed-batch penicillin cultivation process simulation. The diagnostic performance of the proposed approach is significantly better compared to MPCA based approaches. © IFAC.