The ability to manage large image databases has been a topic of growing research over the past several years. Imagery is being generated and maintained for a large variety of applications including remote sensing, art galleries, architectural and engineering design, geographic information systems, weather forecasting, medical diagnostics, and law enforcement. Content-based image retrieval (CBIR) represents a promising and cutting-edge technology that is being developed to address these needs. To date, little work has been accomplished to apply these technologies to the manufacturing environment. Imagery collected from manufacturing processes have unique characteristics that can be used in developing a manufacturing-specific CBIR approach. For example, a product image typically has an expected structure that can be characterized in terms of its redundancy, texture, geometry, or a mixture of these. Defect objects in product imagery share a number of common traits across product types and imaging modalities as well. For example, defects tend to be contiguous, randomly textured, irregularly shaped, and they disrupt the background and the expected pattern. We will present the initial results of the development of a new capability for manufacturing-specific CBIR that addresses defect analysis, product quality control, and process understanding in the manufacturing environment. Image data from the semiconductor-manufacturing environment will be presented.