The increasing need to find alternative stocks of critical raw materials drives to revisit the residues generated during the former production of mineral and metallic raw materials. Geophysical methods contribute to the sustainable characterization of metallurgical residues inferring on their composition, zonation and volume(s) estimation. Nevertheless, more quantitative approaches are needed to link geochemical or mineralogical analyses with the geophysical data. In this contribution, we describe a methodology that integrates geochemical and geophysical laboratory measurements to interpret geophysical field data solving a classification problem. The final aim is to estimate volume(s) of different types of materials to assess the potential resource recovery. We illustrate this methodology with a slag heap composed of residues from a former iron and steel factory. First, we carried out a 3D field acquisition using electrical resistivity tomography (ERT) and induced polarization (IP), based on which, a sampling survey was designed. We conducted laboratory measurements of ERT, IP, spectral induced polarization (SIP), and X-ray fluorescence analysis, based on which, 4 groups of different chemical composition were identified. Then we carried out a 3D probabilistic classification of the field data, based on 2D kernel density estimators (for each group) fitted to the inverted data collocated with the samples. The estimated volumes based on the classification model were: 4.17 × 103 m3 ± 12 %, 1.888 × 105 m3 ± 12 %, 59.4 × 103 m3 ± 19 %, and 2.30 × 104 m3 ± 21% for the groups ordered with an increasing metallic content. The uncertainty ranges were derived from comparing the volumes with and without considering the probabilities associated to the classification. We found that a representative sampling and the definition of the KDE bandwidths are defining elements in the classification and ultimately the estimation of volumes. This methodology is suitable to quantitatively interpret geophysical data in terms of the geochemical composition of the materials, integrating uncertainties both in the classification and the estimation of volumes. Furthermore, several crucial elements in the investigation of metallurgical residues could be applied in a real case study, e.g., geophysical field acquisition, sampling and lab measurements.