Low-cost methods exist for measuring physiology when clinically assessing adolescent social anxiety. Two barriers to widespread use involve lack of (a) physiological expertise among mental health professionals, and (b) techniques for modeling individual-level physiological profiles. We require a “bridge approach” for interpreting physiology that does not require users to have a physiological background to make judgments, and is amenable to developing individual-level physiological profiles. One method—Chernoff Faces—involves graphically representing data using human facial features (eyes, nose, mouth, face shape), thus capitalizing on humans’ abilities to detect even subtle variations among facial features. We examined 327 adolescents from the Tracking Adolescents’ Individual Lives Survey (TRAILS) study who completed baseline social anxiety self-reports and physiological assessments within the social scenarios of the Groningen Social Stressor Task (GSST). Using heart rate (HR) norms and Chernoff Faces, 2 naïve coders made judgments about graphically represented HR data and HR norms. For each adolescent, coders made 4 judgments about the features of 2 Chernoff Faces: (a) HR within the GSST and (b) aged-matched HR norms. Coders’ judgments reliably and accurately identified elevated HR relative to norms. Using latent class analyses, we identified 3 profiles of Chernoff Face judgments: (a) consistently below HR norms across scenarios (n = 193); (b) above HR norms mainly when speech making (n = 35); or (c) consistently above HR norms across scenarios (n = 99). Chernoff Face judgments displayed validity evidence in relation to self-reported social anxiety and resting HR variability. This study has important implications for implementing physiology within adolescent social anxiety assessments.