In this paper, content-based image retrieval from a hierarchically organized database (HCBIR) is proposed. Images in the database are categorized into different classes based on human perception. The characteristics of each class is represented by the prototypes extracted from images in the class by using the unsupervised optimal fuzzy clustering algorithm. Based on the proposed image-class matching distance, a modification of the Earth Mover's Distance, the relevant class of the query image can be selected. The rank of candidate images is determined in the descending order of similarity, and a class with the most number of high ranking images is then selected. The search domain is narrowed down and the retrieval efficiency is improved greatly. A comparison is done between HCBIR approach and nonhierarchical CBIR approach. It can be concluded that the HCBIR approach is believed more similar to the process of human vision, and more efficient.