Summary: This paper illustrates the process monitoring strategy for a multistage manufacturing facility with the aid of cluster analysis and multiple multi-block partial least squares (MBPLS) models. Traditionally, a single MBPLS model is used for monitoring multiple process and quality characteristics. However, modelling all the responses together in a single model may cause poor model fit in the events of: (i) uncorrelated response variables; and (ii) groups of response variables having high correlation amongst the variables within a group but no or negligible correlations between the groups. This paper overcomes this problem by combining cluster analysis with MBPLS through development of multiple MBPLS models. Each of the MBPLS models is used to detect out-of-control observations and a superset of the out-of-control observations is created. Two new fault diagnostic statistics for stage-wise and variable-wise contribution are developed for the superset. The developed methodology is applied to a steel making shop for monitoring. The case study results show that the proposed methodology performs better as compared to the traditionally employed single MBPLS model.