It is estimated that 35% of Americans or 116 million people report experiencing chronic pain daily (Nahin, 2015). Due to the unique and individualized nature of chronic pain, successful treatment of this condition is difficult to achieve. The heterogeneity and complexity in presentation may help explain why, as all methods of treatment have fallen short of providing consistent and reliable outcomes (Finnerup et al., 2015; Lumley & Schubiner, 2019), resulting in unsuccessful treatment approaches that can lead to compounding negative consequences such as hyperalgesia (Garland et al., 2013) and even death (Scholl, Seth, Kariisa, Wilson, & Baldwin, 2019). As such, the current study sought to addresses the need for a more comprehensive assessment protocol to evaluate chronic pain conditions by using natural language analysis to examine how differences in linguistic style reveal important information related to attentional focus, social connectedness, cognitive bias, and other clinical dimensions in a sample of data collected from a pilot study of individuals with chronic pain. This study found that, when compared to population data, significant differences in language use predicted pain severity and pain disability outcomes. Further, when examined at an individual level, data on linguistic style offered incremental information not reliant on self-report measures or subject to self-report bias that may enhance existing approaches to clinical formulations of this multifaceted condition. Taken together, these findings suggest that language use analysis may represent a promising diagnostic tool and assessment measure for better understanding the chronic pain experience at an individual level that improves treatment matching protocols and ultimately overall treatment outcomes.