In this article, participant payment profiles are identified by applying aggregation techniques to TARGET2 data. Payment profiles describe the general payment behavior of participants and are therefore relevant to several risks, such as liquidity, credit and operational risks, that payment systems face. After presenting the challenges of applying cluster analysis techniques to payment data, general payment profiles are derived from the pooled results of multiple runs of the k-means clustering algorithm with different similarity measures. Ten different payment profiles with different general intraday payment behaviors were thus determined. By identifying each participant’s deviations from their profile on a daily basis, the stability of the payment profiles was checked for robustness.