Spinal cord injuries (SCI) result in significant neurological and functional impairments, with current assessments like the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) showing limited sensitivity in detecting residual motor control. Surface electromyography (sEMG) is a sensitive, non-invasive, and quantitative technique to measure muscle activity, even when there are no visible muscle contractions. In order to advance the use of sEMG in the management of SCI, this study aimed to identify distinct muscle electrophysiological profiles from sEMG features in individuals with cervical SCI, hypothesizing the existence of identifiable clusters within sEMG feature space. Using clustering algorithms such as k-means, k-medoids, DBSCAN, and hierarchical clustering, distinct and reproducible clusters were identified from curated sEMG feature sets with commonly used sEMG features in time and frequency domains. The clusters were then correlated with clinical indicators of neurological and muscle function. While significant associations were demonstrated with clinical variables including myotome level, neurological level of injury, and manual muscle testing scores, the structure in the sEMG feature space revealed by the clustering analysis was not fully accounted for by the clinical variables. These findings suggest that sEMG provides complementary information and could enhance assessments and guide clinical treatments for individuals with spinal cord impairments. Innovative tools to stratify patient groups based on detailed electrophysiological profiles may offer significant implications for clinical trial design.