Background: A practical, easy to use model was developed to stratify risk groups in surgical patients: the Identification of Risk In Surgical patients (IRIS) score. Methods: Over 15 years an extensive database was constructed in a general surgery unit, containing all patients who underwent general or trauma surgery. A logistic regression model was developed to predictmortality. This model was simplified to the IRIS score to enhance practicality. Receiver operating characteristic (ROC) curve analysis was performed. Results: The database contained a consecutive series of 33 224 patients undergoing surgery. Logistic regression analysis gave the following formula for the probability of mortality: P (mortality) = A/(1 + A), where A = exp (-4•58 + (0•26 × acute admission) + (0•63 × acute operation) + (0•044 × age) + (0•34 × severity of surgery)). The area under the ROC curve (AUC) was 0•92. The IRIS score also included age (divided into quartiles, 0-3 points), acute admission, acute operation and grade of surgery. The AUC predicting postoperative mortality was 0•90. Conclusion: The IRIS score accurately predicted mortality after general or trauma surgery. Copyright © 2010 British Journal of Surgery Society Ltd. Published by JohnWiley & Sons Ltd.