The operation of transitions in continuous processes is challenging and often results in out-of-spec products, alarm floods, and abnormal situations. Therefore, efficient techniques for automated monitoring and fault diagnosis of such operations are essential. In Part 1 of this paper, we proposed a self-organizing map (SOM) training strategy for effectively visualizing multistate operations. In this part of the series, we use the same methodology as a representation scheme to compare operating trajectories and diagnosing faults during transient operations. In the proposed approach, clusters of SOM neurons, called neuronal clusters, serve as landmarks on the multivariate measurement space. Online data during the transition are reflected as a trajectory on the SOM and are converted to a sequence of neuronal clusters, which are the signature of the operating state. We have adapted the well-known Smith and Waterman discrete sequence comparison algorithm from bioinformatics to compare the state signatures and account for run-to-run variations. The proposed comparison method accounts explicitly for oscillations that are common in chemical processes. Online monitoring and diagnosis is performed by comparing the signature with those of known normal and abnormal transitions. The key advantage of the proposed strategy are its computational speed, inherent multivariate nature, and robustness to run-to-run variations, in addition to intuitiveness and visualization of the results. We illustrate the proposed method through two case studies: the Tennessee Eastman challenge problem and startup of a laboratory-scale distillation unit.