Generic algorithms for analysis of spectral data obtained from a variety of remote sensing platforms have significantly altered the way in which these data are used. Detection of land use change over small areas (100s of square meters), identification of diagnostic landscape features, and differentiation of cropping systems from spectral data sets are more effectively employed when computer aided analysis systems are used. Application of spectral analysis programs such as the Generic Image Enhancement (GENIE) system optimizes data analysis with minimal input, and the system can “learn” the means to identify objects or land uses of interest. This type of analysis greatly reduces the amount of labor required to derive the same information by more labor-intense analyses of the data. Applied at the scale of intact soil cores, this approach can be used to recognize the patterns of constituents that contribute to soil bulk density. Low magnification digital images of soils were collected with duplicate samples for bulk density measurements. Initial analyses of the digital imagery indicates that there is good correlation between measured bulk densities and the analytical algorithms used to recognize the patterns of soil structural components, organic residue, coarse fragments, and voids in images. The algorithms then translate to estimates of soil bulk density, and these estimates come at a fraction of the time required for more conventional analyses.