Detailed urban land cover maps are increasingly useful and important applications of remote sensing. Municipal agencies and others use land cover maps and data for numerous critical local planning and monitoring functions and for urban geographical research studies. Because of this, there is a demand for accurate urban land cover maps that can be produced quickly and economically. The availability of very high resolution multispectral imagery is an important factor in enabling such production, as the judicious selection of source imagery has a large impact on the resulting map products. Likewise, the implementation of appropriate digital image processing methods is crucial for deriving urban land cover maps of acceptable accuracy and cost. This study compared two common image classification algorithms using 2006 NAIP 1-meter GSD CIR images of Orem and Provo, Utah. The two classification procedures – conventional per-pixel supervised classification coupled with post-classification filtering, and object-based feature extraction – were compared for resulting accuracy and, in general terms, for their cost-effectiveness. Results demonstrated that object-based feature extraction has the potential to produce maps with better accuracy, but at a somewhat higher cost than per-pixel supervised classification. Classification errors and their probable causes are discussed; also a number of options for improving the classification accuracy are presented together with considerations of the potential costs involved. Although the ultimate goal of economical production of accurate urban land cover maps was not fully realized, this study nevertheless has established a cost containment baseline upon which methodological improvements can be built.