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Monitoring threatened species at the individual level is fundamental and crucial in ecology and conservation, especially for critically endangered species. Hainan gibbon (Nomasucs hainanus) is the world's rarest primate species with <40 individuals, urgently needs a labor-efficient and continuous individual-level monitoring system to inform science-based conservation actions. Male dispersal, replacement, and formation of new groups with females are crucial for the population dynamics of gibbons. Their loud and elaborate morning songs provide the potential to automate the monitoring of their dynamics. Based on acoustic recordings from focal recording and 13 autonomous recording units within gibbons' home ranges, we developed an automated system, which successively (a) detected song bouts (duration ≈ 10 min, with ≈ 50 phrases) with temporal parameters using an energy detector with a hidden Markov model (F1 score >75 %), (b) detected and classified phrases (duration ≈ 5 s) within song bouts using a random forest model (F1 score ≈ 80–90 %), and (c) classified known individuals by a linear support vector machine (accuracy >93 %), and recognized unknown individuals by Gaussian mixture models (accuracy >90 %). The system successfully monitored a male immigration and kept tracking a dispersing male for nearly a year since it left the natal group to establish its own group with two females. Our system is effective even in the face of an ever-changing population and can be applied to passive acoustic data. It will contribute to the realization of long-term, large-scale, and continuous monitoring of individual dynamics for vocal animals. • We identified and tracked the dispersal of male Hainan gibbons with an automated system. • The system needs little training data and is robust against data variations. • The system helps to monitor individual dynamics from passive acoustics continuously. [ABSTRACT FROM AUTHOR]