Antimicrobial peptides are small peptides encoded by genes. The research area of antimicrobial peptides has attracted intense attention in recent years because “their potential use in the cure of infectious diseases caused by pathogens that have become counteractive to traditional antibiotics” (Boman 1994). There exist huge amount of antimicrobial peptides research articles and this number is continuously increasing. Although some biomedical databases, such as PubMed, have been well established, they provide only query-based information retrieval and end-users need to manually find out relevant information from thousands of retrieved articles. The objective of this paper is to apply one of the text mining techniques, document clustering, which groups similar documents into clusters, to text documents collected from PubMed using keyword “antimicrobial peptides”. The results of our work can help researchers to discover meaningful groups of antimicrobial peptides articles in an efficient manner.