PURPOSE: To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression. DESIGN: Cross-sectional and longitudinal study. PARTICIPANTS: A total of 2231 abnormal VFs from 205 eyes of 176 OHTS participants followed over approximately 16 years. METHODS: VFs were assessed by an unsupervised deep archetypal analysis algorithm as well as an OHTS certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified. MAIN OUTCOME MEASURES: Machine-expert correspondence and type of patterns of VF loss associated with rapid progression. RESULTS: The average VF mean deviation (MD) at conversion to glaucoma was -2.7 dB (Standard Deviation (SD) = 2.4 dB) while the average MD of the eyes at the last visit was -5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of -1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD. CONCLUSIONS: An automated machine learning system can identify patterns of VF loss and could provide objective, and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.