Existing methods of risk adjustment in surgical audit are complex and costly. The present study aimed to develop a simple risk stratification score for mortality and a robust audit tool using the existing resources of the hospital Patient Administration System (PAS) database. This was an observational study for all patients undergoing surgical procedures over a two-year period, at a London university hospital. Logistic regression analysis was used to determine predictive factors of in-hospital mortality, the study outcome. Odds ratios were used as weights in the derivation of a simple risk-stratification model-the Surgical Mortality Score (SMS). Observed-to-expected mortality risk ratios were calculated for application of the SMS model in surgical audit. There were 11,089 eligible cases, under five surgical specialties (maxillofacial, orthopedic, renal transplant/dialysis, general, and neurosurgery). Incomplete data were 3.7% of the total, with no evidence of systematic underreporting. The SMS model was well calibrated [Hosmer-Lemeshow C-statistic: development set (3.432, p = 0.33), validation set (6.359, p = 0.10) with a high discriminant ability (ROC areas: development set [0.837, S.E.=0.013] validation set [0.816, S.E. = 0.016]). Subgroup analyses confirmed that the model can be used by the individual specialties for both elective and emergency cases. The SMS is an accurate risk- stratification model derived from existing database resources. It is simple to apply as a risk-management, screening tool to detect aberrations from expected surgical outcomes and to assist in surgical audit.