Root cause analysis is an important method for fault diagnosis when used with multivariate statistical process monitoring (MSPM). Conventional contribution analysis in MSPM can only isolate the effects of the fault by pinpointing inconsistent variables, but not the underlying cause. By integrating reconstruction-based multivariate contribution analysis (RBMCA) with fuzzy-signed directed graph (SDG), this paper developed a hybrid fault diagnosis method to identify the root cause of the detected fault. First, a RBMCA-based fuzzy logic was proposed to represent the signs of the process variables. Then, the fuzzy logic was extended to examine the potential relationship from causes to effects in the form of the degree of truth (DoT). An efficient branch and bound algorithm was developed to search for the maximal DoT that explains the effect, and the corresponding causes can be identified. Except for the need to construct an SDG for the process, this method does not require historical data of known faults. The usefulness of the proposed method was demonstrated through a case study on the Tennessee Eastman benchmark problem.