The purpose of this study was to develop a Cancer Pain Prognostic Scale (CPPS) which could predict the likelihood of pain relief within 2 weeks for cancer patients with moderate to severe pain. Seventy-four (74) consecutive patients who presented with cancer-related pain were managed in accordance with the guidelines for pain management developed by the United States Agency for Health Care Policy and Research (AHCPR). Patients were followed weekly using the Brief Pain Inventory (BPI), and medications were recorded weekly for 3 weeks. Baseline scores from the Functional Assessment of Cancer Therapy (FACT-G), Mental Health Inventory (MHI), Karnofsky Performance Status (KPS), and Memorial Symptom Assessment Scale Short Form (MSAS-SF) at initial interview served as explanatory variables in a logistic regression model. Pain relief > or = 80% at the end of weeks 1 and 2 were used as outcomes in this model. From this analysis, we developed a predictive formula, the CPPS, which includes the worst pain severity, FACT-G emotional well being, daily opioid dose, and pain characteristics. The rule yields a numerical score that ranges from 0-17. Higher scores correspond to a higher probability of good pain relief. The CPPS has the potential to rapidly identify patients with poor pain prognosis. It can be used as a research tool to characterize pain in cancer patients.