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Background: Non‐motor symptoms are well established phenotypic components of adult‐onset idiopathic, isolated, focal cervical dystonia (AOIFCD). However, improved understanding of their clinical heterogeneity is needed to better target therapeutic intervention. Here, we examine non‐motor phenotypic features to identify possible AOIFCD subgroups. Methods: Participants diagnosed with AOIFCD were recruited via specialist neurology clinics (dystonia wales: n = 114, dystonia coalition: n = 183). Non‐motor assessment included psychiatric symptoms, pain, sleep disturbance, and quality of life, assessed using self‐completed questionnaires or face‐to‐face assessment. Both cohorts were analyzed independently using Cluster, and Bayesian multiple mixed model phenotype analyses to investigate the relationship between non‐motor symptoms and determine evidence of phenotypic subgroups. Results: Independent cluster analysis of the two cohorts suggests two predominant phenotypic subgroups, one consisting of approximately a third of participants in both cohorts, experiencing increased levels of depression, anxiety, sleep impairment, and pain catastrophizing, as well as, decreased quality of life. The Bayesian approach reinforced this with the primary axis, which explained the majority of the variance, in each cohort being associated with psychiatric symptomology, and also sleep impairment and pain catastrophizing in the Dystonia Wales cohort. Conclusions: Non‐motor symptoms accompanying AOIFCD parse into two predominant phenotypic sub‐groups, with differences in psychiatric symptoms, pain catastrophizing, sleep quality, and quality of life. Improved understanding of these symptom groups will enable better targeted pathophysiological investigation and future therapeutic intervention., We demonstrate that individuals with adult‐onset idiopathic, isolated, focal cervical dystonia parse into two subgroups dependent on their non‐motor symptoms. Approximately a third of participants showed increased levels of depression, anxiety, sleep impairment and pain catastrophising, as well as decreased quality of life, indicating that improved understanding of these symptom groups will lead to better targeted treatment plans.