Introduction: Potentials measured at the epicardial surface contain information regarding the conductive properties of the atrial tissue. The current lack of morphological categorization during atrial fibrillation (AF) provokes the usage of unsupervised learning methods to evaluate time series electrograms across the atrial surface. Analysis of regional potential morphology increases insight in individual tissue characteristics, possibly aiding in future selection of patients with AF benefitting from additional treatment with. Methods: single potentials (SP) are extracted from four defined regions: right atrium (RA), left atrium (LA), pulmonary veins (PV) and Bachmann’s bundle (BB). The methods are divided in two parts. In part one, the clusters are determined. Choice of algorithm, evaluation of distance metrics and evaluation in reduced dimensionality are performed sequentially to determine the eventual clustering setup. The clustering setup is repeated for different types of input data, originating from different regional structures (all four regions, solely LA and solely the RA) to avoid deterioration of cluster formation from single region(s). In the second part, the clusters are adapted to evaluate morphological characteristics in the included regions. Results: Data from a total of 23 patients was used, containing 128 files and 468588 SPs. K-means using the first 8 principal components was deemed the most suitable clustering method with the current data, identifying a total of five clusters. The clusters showed evident different morphology, mostly distinguishable by RS-ratio and potential duration. RA showed a predominant S to an RS-wave pattern, comparable to PV. Both showed similar contribution of fast and slow S-wave morphology. LA showed a RS to R-wave morphology. The cluster showing a slow S-wave morphology was substantially increased across BB compared to other regions but also when compared to other clusters in BB. Conclusion: Looking at spatial cluster occurrence through an RS-ratio perspective, the beforehand expected deviation from AF compared to sinus rhythm (SR) was not observed. The observed pattern during AF suggests more elaborate contribution of atrial tissue than often targeted in current daily practice. The methodology and defined groups of potentials facilitate evaluation of potential morphology at an individual level. Translation to an endocardial perspective could assist in selecting patients eligible for additional treatment when technologically feasible. Future research should be focused on including potentials containing multiple deflections and estimation of progression state of the atrial tissue.