Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and othermarkers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developedan automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast CancerAssociation Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breastcancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs werestained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariolsystem. An automated algorithm was developed for the scoring of Ki67, and scores were compared to com-puter assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlationbetween automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed gooddiscriminatory accuracy (AUC585%) and good agreement (kappa50.64) between the automated and CAVscoring methods in the training set. The performance of the automated method varied by TMA (kappa range50.37–0.87) and study (kappa range50.39–0.69). The automated method performed better in satisfactorycores (kappa50.68) than suboptimal (kappa50.51) cores (p-value for comparison50.005); and amongcores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa50.78) than those withlower counts (50–500 cells: kappa50.41;p-value50.010). Among the 9,059 cases in this study, the corre-lations between automated Ki67 and clinical and pathological characteristics were found to be in the expecteddirections. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain goodquality data across large numbers of TMAs from multicentre studies. However, robust algorithm developmentand rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactoryperformance.