In this paper, fault diagnosis based on patterns exhibited in the sensors measuring the process variables is considered. The temporal patterns that a process event leaves on the measured sensors, called event signatures, can be utilized to infer the state of operation using a pattern-matching approach. However, the qualitative nature of the features leads to imprecise classification boundaries at the trend-identification stage and hence at the trend-matching stage. Moreover, noise and other underlying phenomena may lead to non-reproducibility of the same trends chosen to represent an event. Thus, a crisp inference process might lead to a large knowledge-base of signatures; it could also cause misclassification. To overcome this, a fuzzy-reasoning approach is proposed to ensure robustness to the inherent uncertainty in the identified trends and to provide succinct mapping. A two-staged strategy is employed: (i) identifying the most likely fault candidates based on a similarity measure between the observed trends and the event-signatures in the knowledge-base and, (ii) estimation of the fault magnitude. The fuzzy-knowledge-base consists of a set of physically interpretable if-then rules providing physical insight into the process. The technique provides multivariate inferencing and is transparent. We illustrate the application of the proposed approach in the fault diagnosis of an exothermic reactor case study.