A large number of samples of visually classified channel networks are taken from some published works. The samples are representative of the following pattern types: dendritic, parallel, rectangular, trellis, and pinnate. By means of computer techniques, essential parameters of elements of these pattern samples are determined. These include lengths, directions, and degrees of curvedness and meandering of channel segments, as well as confluence angles. The parameters are used to determine pattern attributes such as density, texture, parallelism, rectangularity, and bifurcation ratios. Thresholds are generated for the attributes and used in the construction of classification models for the five pattern types studied. The processes and models are coded in a computer program for use in the automated classification of numerically valued channel networks. After classification the image is displayed with each individual network bearing a color which shows the pattern type to which it has been assigned.