Different classification methods have been used to categorize the digital halftone algorithms and to help match the algorithms to the printer capabilities. The existing classification methods use the dot distribution, the dot generation process or a general description of the halftone pattern power spectra as basis for classification. Recent progress in understanding the interaction between the binary halftone patterns and the printer capabilities suggests that an optimal classification method may be based on the halftone pattern morphology. In this study, we review the existing classification methods, and introduce an alternative method based on the morphological similarities of binary halftone patterns. The method uses topological measurements of the halftone pattern to describe its morphology. The simplicity of the method permits its application to various areas of printing research such as printer characterization, toner deposition studies and development of printer models. As an example, we present a case study where we compared our proposed classification method with a commonly used method in their application to the development of a printer model. In this comparison our morphologically based classification method yielded improved printer predictions.