SUMMARY: Modern geophysical data acquisition technology makes it possible to measure multiple geophysical properties with high spatial density over large areas with great efficiency. Instead of presenting these co-located multigeophysical data sets in separate maps, we take advantage of cluster analysis and its pattern exploration power to generate a cluster map with objectively integrated information. Each cluster in the resulting cluster map is characterized by multigeophysical properties and can be associated with certain geological attributes or rock types based on existing geological maps, field data and rock sample analysis. Such a cluster map is usually high in resolution and proven to be more helpful than single-attribute maps in terms of assisting geological mapping and interpretation. In this paper, we present the workflow and technical details of applying cluster analysis to multigeophysical data of a study area in the Trøndelag region in Mid-Norway. We address the importance of carefully designed pre-processing procedures regarding the input data sets to ensure an unbiased data integration using cluster analysis. Random forest as a supervised machine learning method for classification/regression is strategically employed post-clustering for quality evaluation of the results. The multigeophysical data used for this study include airborne magnetic, frequency electromagnetic and radiometric measurements, together with ground gravity measurements. Due to the nature of these input data, the resulting cluster map carries multidepth information. When associated with available geological information, the cluster map can help interpret not only bedrock outcrops but also rocks underneath the sediment cover.