Visual characterization of histologic specimens is known to suffer from intra- and inter-observer variability. To help address this, we developed an automated framework for characterizing digitized histology specimens based on a novel application of color histogram and color texture analysis. We perform a preliminary evaluation of this framework using a set of 73 trichrome-stained, digitized slides of normal breast tissue which were visually assessed by an expert pathologist in terms of the percentage of collagenous stroma, stromal collagen density, duct-lobular unit density and the presence of elastosis. For each slide, our algorithm automatically segments the tissue region based on the lightness channel in CIELAB colorspace. Within each tissue region, a color histogram feature vector is extracted using a common color palette for trichrome images generated with a previously described method. Then, using a whole-slide, lattice-based methodology, color texture maps are generated using a set of color co-occurrence matrix statistics: contrast, correlation, energy and homogeneity. The extracted features sets are compared to the visually assessed tissue characteristics. Overall, the extracted texture features have high correlations to both the percentage of collagenous stroma (r=0.95, p<0.001) and duct-lobular unit density (r=0.71, p<0.001) seen in the tissue samples, and several individual features were associated with either collagen density and/or the presence of elastosis (p≤0.05). This suggests that the proposed framework has promise as a means to quantitatively extract descriptors reflecting tissue-level characteristics and thus could be useful in detecting and characterizing histological processes in digitized histology specimens.