Increasing antibiotics resistance in human pathogens represents a pressing public health issue worldwide for which novel antibiotic therapies based on antimicrobial peptides (AMPs) may offer one possible solution. In the current study, we utilized publicly available data on AMPs to construct hidden Markov models (HMMs) that enable recognition of individual classes of antimicrobials peptides (such as defensins, cathelicidins, cecropins, etc.) with up to 99% accuracy and can be used for discovering novel AMP candidates.