Eighty-nine patients with chronic liver disease were selected as the subject for this study. These patients were found to have some abnormalities in health check-ups and confirmed finally by biopsy or laparoscopy findings. As healthy controls, 1633 subjects who had no abnormalities in health checkups, were selected. Serum specimens in the fasting state were collected from all of these subjects, and twenty chemical items were assayed by an automatic analyzer. Data of the control group were transformed for each item and both sexes into the normal distribution by adopting the most suitable mathematical processing, and the standard deviation indices, SDI = (Xp - Xn)/SDn, were calculated. The items specific to each disease were decided by selecting those showing significant differences from the control group both in mean values and in distribution of SDI. Using these items, the specific SDI-patterns were obtained. By such processing it has been possible, to show every item on the same yardstick, to find significant deviations even within the normal range more quantitatively, and to find overall abnormalities of a subject at a glance. On account of these characteristic advantages, it is clear that this pattern presentation is efficient for the early recognition of pathophysiological states of subjects in health check-ups.