Identification of subjects at high risk of coronary morbidity is of major interest in the prevention of cardiovascular disease. This report describes the use of a multifactorial prediction model for the identification of high risk subjects in a French male population. The PCV-METRA study (Prévention Cardiovasculaire en Médecine du Travail) monitors risk factors of cardiovascular morbidity in a population of men and women employed in big companies in the Paris region. A model adapted from a prediction model conceived by K.M. Anderson et al. in the Framingham study was used. The modified model enables an estimation of individual coronary risk based on 7 factors: age, total cholesterol, HDL-cholesterol, systolic blood pressure, smoking, diabetes and presence of left ventricular hypertrophy, taking into account the relatively low prevalence of coronary heart disease in France. The population comprised 4,131 active men aged 30 to 65 years. The average risk at 5 years was estimated to be 1.6%. Subjects at high risk (over the 80th percentile of the risk distribution curve) usually had high blood pressures and cholesterol levels. However, nearly 30% of these subjects were neither hypertensive nor hypercholesteraemic. It is important to note that 3/4 of these smoked. Moreover, they also had low HDL-cholesterol levels. A risk table, derived from the Framingham model, is presented. This table allows estimation of individual risk at 5 years in men aged 30 to 65 years. In each age group, the comparison of individual risk with the percentiles of risk distribution in the PCV-METRA population allows identification of high-risk subjects. This study proposes a tool for identifying subjects at high risk of coronary morbidity in a French male population. This multifactorial model is particularly useful for detecting subjects with several borderline factors none of which overstep the usually accepted limits.