The purpose of this study was to develop a multiple linear regression model to predict treadmill VO2max scores using both exercise and nonexercise data. One hundred five college-aged participants (53 male, 52 female, mean age 23.5 ± 2.8 yrs) successfully completed a submaximal cycle ergometer test and a maximal graded exercise test (GXT) on a motorized treadmill. The submaximal cycle protocol required participants to achieve a steady-state heart rate (HR) equal to at least 70% of age-predicted maximum HR (220-age), while the maximal treadmill GXT required participants to exercise to volitional fatigue. Relevant submaximal cycle ergometer test data included a mean (± SD) ending steady-state HR and ending workrate equal to 164.2 ± 13.0 and 115.3 ± 27.0, respectively. Relevant nonexercise data included a mean (± SD) body mass (kg), perceived functional ability [PFA] score, and physical activity rating [PA-R] score of 74.2 ± 15.1, 15.7 ± 4.3, and 4.7 ± 2.1, respectively. Multiple linear regression was used to generate the following prediction of cardiorespiratory fitness (R = 0.91, SEE = 3.36 ml∙kg-¹∙min-¹): VO2max = 54.513 + 9.752 (gender, 1 = male, 0 = female) − 0.297 (body mass, kg) + 0.739 (PFA, 2-26) + 0.077 (work rate, watts) − 0.072 (steady-state HR). Each predictor variable was statistically significant (p < .05) with beta weights for gender, body mass, PFA, exercise workrate, and steady-state HR equal to 0.594, -0.544, 0.388, 0.305, and -0.116, respectively. The predicted residual sums of squares (PRESS) statistics reflected minimal shrinkage (RPRESS = 0.90, SEEPRESS = 3.56 ml∙kg-¹∙min-¹) for the multiple linear regression model. In summary, the submaximal cycle ergometer protocol and accompanying prediction model yield relatively accurate VO2max estimates in healthy college-aged participants using both exercise and nonexercise data.