Many industrial pharmaceutical manufacturing processes are composed of multiple-step batch operations. However, uncontrolled variations often occur during operations that affect the cell growth performance. Because of the complexity of biological processes, one leading challenge in process operation is the identification of potential causes of undesirable process variabilities. In this paper, we propose a classification and diagnosis strategy to analyze cell culture manufacturing variability in bioprocesses with the objective of unveiling hidden factors affecting process yield and performance. The proposed strategy includes two parts: (i) a clustering method performed in the principal component residuals that effectively separates the low lactate batches into different clusters and (ii) a fault diagnosis method based on regularized LDA contribution analysis for exploring the leading contributors to each of the low performance classes. The proposed strategy is applied to industrial production data collected over 8 years from a batch process. The effectiveness of the proposed approach is demonstrated on this data set, allowing the classification and diagnosis of the sources of the low performance situations.