Due to the advance of the distributed control systems in the chemical industry, the number of alarms per operator has increased drastically. A poor alarm system might cause sequential alarms, which are triggered in succession by a single root cause in a chemical plant. In this paper, we propose a method for identifying sequential alarms hidden in plant operation data by using dot matrix analysis. Dot matrix analysis is one of the sequence alignment methods for identifying similar regions in a pair of DNA or RNA sequences. The proposed method first converts plant operation data recorded in a distributed control system into a single alarm sequence by putting alarms in order of occurrence time. Next, regions similar to each other in the alarm sequence are identified. Finally, the identified regions, which are assumed to be sequential alarms, are classified into sets of similar sequential alarms in accordance with the similarities between them. The method was applied to simulated plant operation data of an azeotropic distillation column. The results showed that the method is able to correctly identify sequential alarms in plant operation data. Classifying sequential alarms into small numbers of groups with this method effectively reduces unimportant sequential alarms in industrial chemical plants.