Previous investigators reported that peak oxygen uptake (&OV0312;O2peak) could be accurately predicted from nonexercise test variables, and that this score would be suitable for categorizing cardiorespiratory fitness (CRF) within epidemiological studies. However, the accuracy of these models has varied considerably. The purposes of this study were: 1) assess the accuracy of predicting &OV0312;O2peakwith a new nonexerise model, and 2) assess the utility of the predicted &OV0312;O2peakfor categorizing CRF in epidemiological studies. Subjects included 2,350 men and women. Cross-validated multiple regression models revealed that age, sex, resting heart rate, body weight, percentage body fat, smoking, and physical activity were significant predictors (P< 0.001) of &OV0312;O2peak. The multiple regression model for relative &OV0312;O2peak(ml·kg−1.min−1) had R2= 0.733 (SEE = 5.38), whereas the model for absolute &OV0312;O2peak(1.min−1) had R2= 0.773 (SEE = 0.425). The 95% confidence intervals for the predicted &OV0312;O2peakwere large (± 10.6 ml·kg−1.min−1and ± 0.833 1.min−1). These results support the notion that &OV0312;O2peakcan be predicted from a multiple regression model devoid of exercise test variables. However, due to the extreme variability in the predicted scores, the regression models were unable to effectively distinguish CRF categories. Therefore, despite statistical success in predicting &OV0312;O2peakfor the nonexercise test regression models, we conclude that such models fail to provide the accuracy needed for categorizing CRF within large epidemiological cohorts where the purpose is to assess mortality risk.