The modern dairy industry routinely generates data on production and disease. Therefore, the use of these cheap and at times even "free" data to predict a given state of welfare in a cost-effective manner is evaluated in the present study. Such register data could potentially be used in the identification of herds at risk of having animal welfare problems. The present study evaluated the diagnostic performance of four routinely registered indicators for identifying herds with high lameness prevalence among 40 Danish dairy herds. Indicators were extracted as within-herd annual means for a one-year period for cow mortality, bulk milk somatic cell count, proportion of lean cows at slaughter and the standard deviation (SD) of age at first calving. The target condition "high lameness prevalence" was defined as a within-herd prevalence of lame cows of ≥ 16% (third quartile). Diagnostic performance was evaluated by constructing and analysing Receiver Operating Characteristic curves and their area under the curve (AUC) for single indicators and indicator combinations. Sensitivity (Se) and specificity (Sp) of the indicators were assessed at the optimal cut-off based on data and compared to a set of predefined cut-off levels (national annual means or 90-percentile). Cow mortality had the highest AUC (0.76), while adding the three other indicators to the model did not yield significant increase in AUC. Cow mortality and SD of age at first calving had highest Se (100%, 95% confidence interval (CI): 72-100%), while highest Sp was found for the proportion of lean cows at slaughter (83%, 95% CI: 66-93%). The highest differential positive rate (DPR = 0.53) optimizing both Se and Sp was found for cow mortality. Optimal cut-off points were lower than the presently used pre-defined cut-offs. The selected register-based indicators proved to be able to identify herds with high lameness prevalences. Optimized cut-offs improved the predictive ability and should therefore be preferred in official control schemes.