The analysis of stable isotopes in archaeological human and animal skeletal finds is a routine method in the field of archaeometry. Multi-dimensional datasets are this way easily generated what, however, demands new interpretive approaches. Cluster analyses are capable of detecting multi-dimensional similarities among the isotopic signatures and are thus superior over traditional uni- and bivariate evaluations. This study aims to demonstrate the potential of Gaussian Mixture Model (GMM) clustering for palaeodiet reconstruction. We reevaluate previously published isotopic datasets obtained on early medieval skeletal finds from Upper Bavaria (Germany), also including some individuals from "special burials", e.g., from separated burial sites or circular enclosures. This manageable and easy-to-understand dataset contains four-dimensional isotope signatures (δ13Ccollagen, δ15N, δ13Ccarbonate, δ18Ocarbonate) of 27 individuals. We illustrate the iterative process of cluster analysis and the additional benefit of its outcome for the interpretation of the data. This includes the recognition of noisy isotope data and of those isotope systems that are essential. In our example, three isotope systems were required for a correct palaeodiet reconstruction. While δ13Ccollagen and δ15N values are commonly used for palaeodiet reconstructions, δ13Ccarbonate values, including additional information on dietary carbohydrates and fats, are required for a meaningful cluster separation and, thus, the interpretation of the individuals' diet. No general correlation between dietary quality and grave goods was detectable.