Although various models have been proposed in an attempt to predict the usefulness of a radiographic image in terms of its physical characteristics, no previous work has shown whether a single physical image quality index, such as a signal-to-noise ratio, can reliably predict the performance of a human observer over a broad range of image characteristics. We studied the relationship between physical and visual image quality for the task of detecting nylon beads in radiographs. Thirty-seven imaging cases with different combinations of physical image characteristics were considered; these included variations in object size and magnification, X-ray beam quality, screen-film system, screen-film contact, film density and illumination, and viewing distance. For each imaging case, visual image quality was quantified in terms of observer performance in a 2AFC visual detection experiment. Physical image quality indices were calculated according to eight different models of the detection process; these indices combined data regarding object size and attenuation, screen-film system MTF, film gradient, noise Wiener spectrum, and visual system response. The results of this work indicate that, for the conditions studied, human observer detection performance most closely resembles that of a sub-optimal statistical decision process.