The transitions between operational modes (start up/shut down) in chemical processes generate alarm floods and cause critical alarm saturation. We propose in this paper an approach of alarm management based on a diagnosis process. This diagnosis step relies on situation recognition to provide to the operators relevant information on the failures inducing the alarms flows. The situation recognition is based on chronicle recognition where we propose to use the hybrid causal model of the system and the expertise to generate the pattern event sequences from which the chronicles will be extracted using the Heuristic Chronicle Discovery Algorithm Modified HCDAM. An illustrative example in the field of petrochemical plants is presented in the article.