Abstract: OBJECTIVE: The purpose of this study was to apply network analysis methodology to better understand the relationships between pain-related measures among people with chronic pain. METHODS: We analyzed data from a cross-sectional sample of 4,614 active duty service members with chronic pain referred to one military interdisciplinary pain management center between 2014 and 2021. Using a combination of Patient-Reported Outcomes Measurement Information System measures and other pain-related measures, we applied the 'EBICglasso' algorithm to create regularized partial correlation networks that would identify the most influential measures. RESULTS: Pain interference, depression, anxiety had the highest strength in these networks. Pain catastrophizing played an important role in the association between pain and other pain-related health measures. Bootstrap analyses showed that the networks were very stable and the edge weights accurately estimated in two analyses (with and without pain catastrophizing). CONCLUSIONS: Our findings offer new insights about the relationships between symptoms using network analysis. Importantly findings highlight the strength of association between pain interference, depression and anxiety, which suggests that if pain is to be treated depression and anxiety must also be addressed. What was of specific importance was the role that pain catastrophizing had in the relationship between pain and other symptoms suggesting that pain catastrophizing is a key symptom on which to focus for treatment of chronic pain.