Maximal oxygen uptake (VO(2)max) is one of the most important determinants that directly affects the performance of cross-country skiers during races. In this study, various models have been developed to predict the VO(2)max of cross-country skiers by combining different machine learning methods with the Relief-F feature selection algorithm. Machine learning methods used in this study include General Regression Neural Network (GRNN), Gene Expression Programming (GEP), Group Method of Data Handling Polynomial Network (GMDH) and Single Decision Tree (SDT). The predictor variables used to develop prediction models are age, gender, weight, height, heart rate (HR), heart rate at lactate threshold (HRLT) and exercise time. By using 10-fold cross-validation on the dataset, the performance of the prediction models has been evaluated by calculating their multiple correlation coefficient (R) and standard error of estimate (SEE). The results show that the GRNN-based model including all predictor variables yields the highest R (0.92) and the lowest SEE (2.98 ml kg(-1) min(-1)).