The purpose of this study was to develop an age-generalized regression model to predict maximal oxygen uptake (VO sub 2 max) based on a maximal treadmill graded exercise test (GXT; George, 1996). Participants (N = 100), ages 18-65 years, reached a maximal level of exertion (mean plus or minus standard deviation [SD]; maximal heart rate [HR sub max] = 185.2 plus or minus 12.4 beats per minute (bpm); maximal respiratory exchange ratio [RER sub max] = 1.18 plus or minus 0.05; maximal rating of perceived exertion (RPE sub max) = 19.1 plus or minus 0.7) during the GXT to assess VO sub 2 max (mean plus or minus SD; 40.24 plus or minus 9.11 mL-kg super -1.in super -1). Multiple linear regression generated the following prediction equation (R = .94, standard error of estimate [SEE] = 3.18 mL-kg super-1.min super 1, % SEE = 7.9): VOsub 2 max (mL-kg super -1.min -1) = 13.160 + (3.314 x gender; females = 0, males = 1) - (.131 x age) - (.334 x body mass index (BMI)) + (5.177 treadmill speed; mph) + (1.315 treadmill grade; %). Cross validation using predicted residual sum of squares (PRESS) statistics revealed minimal shrinkage (R sub p =.93 and SEE sub p = 3.40 mL-kg super -1.min super -1); consequently, this model should provide acceptable accuracy when it is applied to independent samples of comparable adults. Standardized beta-weights indicate that treadmill speed (.583) was the most effective at predicting VO sub 2max followed by treadmill grade (.356), age (-.197), gender (.183), and BMI (-.148). This study provides a relatively accurate regression model to predict VO sub 2max in relatively fit men and women, ages 18-65 years, based on maximal exercise (treadmill speed and grade), biometric (BMI), and demographic (age and gender) data. (Contains 3 tables and 1 figure.)