Today rapid survey methods of proximal soil sensing (PSS) provide an increasing number of different and highly resolved data. These multidimensional data sets can lead to multilayered and complex maps of parameters which are only indirectly related to soil properties and soil functions. However, in applications usually just one clear elementary map is required. It is of increasing importance to tackle this problem utilizing a cluster algorithm for the synthesis and reduction of multidimensional input variables. The cluster algorithm provides a partitioning of the investigated site whereby the units are characterized by the statistics of the PSS data. Therefore, the question that arises is how suitable is the suggested partitioning in terms of the delineation of different soil units. In this study, we investigate the suitability of cluster partitioning through a case study at a medium-scale test site (≈ 50 000 m2). Two common PSS methods: electromagnetic induction (EMI) and gamma spectrometry (GS) will be employed to create a data set for partitioning by a K-means cluster. The result of the cluster analysis is a delineation of three different parts. In contrast to previous studies, we evaluate the generated partitions by independent soil properties such as grain size, horizon thickness, and color of stratified randomly taken soil samples. The soil analyses show that one of three clusters significantly differs from the others in terms of grain-size distribution and horizon thickness. The partitioning of the other two clusters could not be confirmed by the considered soil parameters. Nevertheless, the case study demonstrates the combination of different PSS data by K-means clustering as a potential approach for site partitioning. An evaluation of the results of the cluster analysis through the collection and analysis of soil samples is highly recommended.