The spatial analysis and interpretation of lithological and geochemical sampling information are central in mineral prospecting and initial geological-mining exploration to delineate exploration targets and locate economic mineralization. This work compares two geostatistical approaches for the spatial prediction of lithological classes through a case study in mineral prospection, considering lithological and geochemical information at a set of surface samples. Both approaches calculate the probabilities of occurrence of the lithological classes at unsampled locations and select the most probable class as the predicted lithology. A split-sample technique is used to assess their performance, with the predictions being made at a testing data subset on the basis of the information of a training subset. The first approach relies on a cokriging of the lithological class indicators and yields an accuracy score (percentage of matches between true and predicted lithological classes) of 90.5%, while the second approach, consisting of a plurigaussian modeling of the classes, increases this score to 92.6%. Unlike the former approach, it also provides consistent outcomes of both the lithological classes and the geochemical covariates, which is valuable for mineral prospectivity mapping.