Purpose: Identifying and monitoring visual field (VF) defects due to optic neuritis (ON) relies on qualitative clinician interpretation. Archetypal analysis (AA), a form of unsupervised machine learning, is used to quantify VF defects in glaucoma. We hypothesized that AA can identify quantifiable, ON-specific patterns (as archetypes [ATs]) of VF loss that resemble known ON VF defects. Methods: We applied AA to a dataset of 3892 VFs prospectively collected from 456 eyes in the Optic Neuritis Treatment Trial (ONTT), and decomposed each VF into component ATs (total weight = 100%). AA of 568 VFs from 61 control eyes was used to define a minimum meaningful (≤7%) AT weight and weight change. We correlated baseline ON AT weights with global VF indices, visual acuity, and contrast sensitivity. For eyes with a dominant AT (weight ≥50%), we compared the ONTT VF classification with the AT pattern. Results: AA generated a set of 16 ATs containing patterns seen in the ONTT. These were distinct from control ATs. Baseline study eye VFs were decomposed into 2.9 ± 1.5 ATs. AT2, a global dysfunction pattern, had the highest mean weight at baseline (36%; 95% confidence interval, 33%-40%), and showed the strongest correlation with MD (r = -0.91; P < 0.001), visual acuity (r = 0.70; P < 0.001), and contrast sensitivity (r = -0.77; P < 0.001). Of 191 baseline VFs with a dominant AT, 81% matched the descriptive classifications. Conclusions: AA identifies and quantifies archetypal, ON-specific patterns of VF loss. Translational Relevance: AA is a quantitative, objective method for demonstrating and monitoring change in regional VF deficits in ON.