With the rapid expansion in imaging technologies, retrieval of images from digital collections is a subject of major interest. However, the large amount of research devoted to understanding mechanisms and products of human visual perception has, by and large, not produced information which is directly applicable to the problems of image retrieval, and systems designers still need basic data concerning which image attributes are most typically noticed by humans when viewing images. The goal of this research was to fill this gap in our knowledge, by investigating attributes reported by participants in several describing tasks with pictorial images. Content analysis of word and phrase data revealed forty-two image attributes and ten higher level classes of attributes. Participants most typically describe the perceptual attributes such as the literal object content of images and the human form, as well as the attribute of color. Location appears to be important and needs to be accounted for, as does a group of interpretive attributes labeled CONTENT/STORY. The research results suggest that term variability in the image descriptions is less than previous research might indicate, and communicative constraints operating on visually perceived data may aid in simplifying some of the approaches necessary to accomplish automated indexing. The initial analysis suggests several conceptual frameworks, such as basic level objects, figure-ground, and the use of schemas, as fruitful approaches to image indexing and retrieval.