Areal proportions of mapped lithologic units are used as independent variables in a regression equation to predict the element content of stream sediments. Geochemical variation due to average differences in chemical composition between lithologic units is removed by linear regression from the data, highlighting anomalies due to mineralization and to secondary processes active in the surficial environment. For the Cobequid Highlands lithologic effects explain between 30% and 65% of the variance, depending on the element examined. The effects of scavenging metal ions by Fe and Mn in the secondary environment are also removed using the same technique. Co, Ni and As show particularly strong associations with Fe and Mn. Regression calculations also show that if geologic map units are recorded as present or absent (1 or 0) in each basin, instead of using area measurements, the proportion of variance explained is almost as large. However, using only the most areally abundant unit in each basin results in about 25% less efficient prediction, while similar results are obtained using only the presence of the map unit closest to each sample point. Co-occurrence of units is represented in the regression equation by using cross-products of lithologic variables in presence/absence form. Co-occurrence of certain map units in a catchment basin appears to be a relatively important factor for predicting Zn. A method for obtaining a weighted mean and weighted dispersion for the element contents characterizing each map unit is proposed. These values are more easily interpreted than those from regression coefficients, which suffer from large variances for map units which cover only small areas. A catchment basin map showing positive Zn anomalies after removing geological background and secondary effects due to Fe and Mn scavenging is shown as a grey-tone image, based on a percentile scale. Digitization of geologic and catchment basin maps and their merging with geographically located geochemical data, allows routine production of colour or grey-tone images depicting measured or derived geochemical parameters and provides a powerful tool for geochemical exploration.