Objective: Therapeutic antibodies have few varieties of side effects due to their high specificity; however, many therapeutic antibodies have serious side effects. A thorough understanding of the side effects is crucial for early recognition and optimal management. To facilitate the understanding of the side effects of therapeutic antibodies, this study attempted to classify therapeutic antibodies based on their side effects using principal component analysis (PCA) and cluster analysis. Method: We collected data on the serious side effects of therapeutic antibodies from package inserts and created a therapeutic antibody-side effect matrix, with therapeutic antibodies as indices and side effects as columns. PCA was performed on the therapeutic antibody-side effect matrix, and hierarchical cluster analysis was performed using principal components. Results: The therapeutic antibodies were classified into four clusters. Cluster 1 included immune checkpoint inhibitors, and featured type 1 diabetes, thyroid disorder, and myasthenia gravis. Cluster 2 included antibodies that inhibit the vascular endothelial growth factor pathway, and featured impaired wound healing, nephrotic syndrome, and thrombosis. Cluster 3 included anti-epidermal growth factor receptor antibodies, and featured diarrhea, hypomagnesemia, and skin disorders. Cluster 4 included other therapeutic antibodies, and featured infection, bone marrow suppression, and hypersensitivity. Conclusion: Therapeutic antibodies can be classified based on their side effects. The results of this study make it easier to understand the side effects of therapeutic antibodies with complex profiles. A better understanding facilitates early detection of side effects and enables high-quality management of side effects.