Machine-learning methods may play an increasing role in the development of precision agriculture tools to provide predictive insights in dairy farming operations and to routinely monitor the status of dairy cows. In the present study, we explored the use of a machine-learning approach to detect and monitor the welfare status of dairy herds in terms of lameness and lesions based on pre-recorded farm-based records. Animal-based measurements such as lameness and lesions are time-consuming, expensive and, thus, typically not collected on a routine basis. A predictive model that is suitable for routine field applications can be thus an efficient strategy to improve dairy cattle welfare. A decision tree approach was therefore used to classify the welfare status of 229 herds. Single measurements were aggregated to a composite index for lameness and lesions, scaled to percentile ranks, and expressed as low, intermediate and high risk that a herd be deficient in lameness and lesions. Routinely collected dairy herd improvement data related to milk production, milk quality, herd size, housing and reproduction were used as potential predictors of the risk level. Model accuracy based on the average of repeated 10-fold cross validation suggests that a simple decision tree algorithm was able to predict welfare level with a mean accuracy of 44%. Ensemble methods such as random forests and boosting methods slightly improved the prediction performance to some extent (up to 51% accuracy). Model specificity for herds at high welfare risk was 91% with a boosting approach, suggesting that only a small proportion of lower risk herds were misclassified as high risk herds. These results suggest that a model based on a machine-learning approach is able to detect herds with potential welfare deficiencies using routine herd data. Additional data are required to improve model performance and validate the approach. Nonetheless, a machine-learning approach may be an appropriate and powerful tool to estimate and monitor the dairy welfare status at herd level, and can be a useful decision support tool for dairy farmers.