Background: The holistic characterization of different microbiomes in anaerobic digestion (AD) systems can contribute to a better understanding of these systems and provide starting points for bioengineering. The present study investigates the microbiome of 80 European full-scale AD systems. Operational, chemical and taxonomic data were thoroughly collected, analysed and correlated to identify the main drivers of AD processes. Results: The present study describes chemical and operational parameters for a broad spectrum of different AD systems. With this data, Spearman correlation and differential abundance analyses were applied to narrow down the role of the individual microorganisms detected. The authors succeeded in further limiting the number of microorganisms in the core microbiome for a broad range of AD systems. Based on 16S rRNA gene amplicon sequencing, MBA03, Proteiniphilum, a member of the family Dethiobacteraceae, the genus Caldicoprobacter and the methanogen Methanosarcina were the most prevalent and abundant organisms identified in all digesters analysed. High ratios for Methanoculleus are often described for agricultural co-digesters. Therefore, it is remarkable that Methanosarcina was surprisingly high in several digesters reaching ratios up to 47.2%. The various statistical analyses revealed that the microorganisms grouped according to different patterns. A purely taxonomic correlation enabled a distinction between an acetoclastic cluster and a hydrogenotrophic one. However, in the multivariate analysis with chemical parameters, the main clusters corresponded to hydrolytic and acidogenic microorganisms, with SAOB bacteria being particularly important in the second group. Including operational parameters resulted in digester-type specific grouping of microbes. Those with separate acidification stood out among the many reactor types due to their unexpected behaviour. Despite maximizing the organic loading rate in the hydrolytic pretreatments, these stages turned into extremely robust methane production units. Conclusions: From 80 different AD systems, one of the most holistic data sets is provided. A very distinct formation of microbial clusters was discovered, depending on whether taxonomic, chemical or operational parameters were combined. The microorganisms in the individual clusters were strongly dependent on the respective reference parameters. Graphical Abstract: (Figure presented.)