The premise of geographic object-based image analysis (GEOBIA) is that image objects are composed of aggregates of pixels that correspond to earth surface features of interest. Most commonly, image-derived objects (segments) or objects associated with predefined land units (e.g., agricultural fields) are classified using parametric statistical characteristics (e.g., mean and standard deviation) of the within-object pixels. The objective of this exploratory study was to examine the between- and within-class variability of frequency distributions of multispectral pixel values, and to evaluate a quantitative measure and classification rule that exploits the full pixel frequency distribution of within object pixels (i.e., histogram signatures) compared to simple parametric statistical characteristics. High spatial resolution Quickbird satellite multispectral data of Accra, Ghana were evaluated in the context of mapping land cover and land use and socioeconomic status. Results show that image objects associated with land cover and land use types can have characteristic, non-normal frequency distributions (histograms). Signatures of most image objects tended to match closely the training signature of a single class or sub-class. Curve matching approaches to classifying multi-pixel frequency distributions were found to be slightly more effective than standard statistical classifiers based on a nearest neighbor classifier.