The ability to review and analyze large amounts of data reliably and cost-effectively is important in both biomedical research and clinical care. We hypothesized that converting raw digital data to standard scores (Z scores) and gray-scale them based on their corresponding P values can be used to accomplish this. In Part 1 of the study, we recorded continuous digital electrocardiographic (ECG) and heart sound data from a subject undergoing acute anterior myocardial infarction (MI). We then computed Z scores of the digital data using the means and standard deviations of the data obtained during the pre-infarction period. In Part 2 of the study, we analyzed the digital ECG data from 576 subjects who had undergone coronary angiography and left ventriculography for the evaluation of possible coronary disease. We used the durations of Q waves in Lead aVF and of the initial R waves in Lead V2 as the ECG criteria of prior inferior and anterior MI, respectively. We calculated Z scores for these durations using the means and standard deviations of the subjects who had no angiographic evidence of coronary disease. Results show that in Part 1 of the study, the continuous recording of the gray-scale Z scores produced a highly intuitive display of the direction and statistical significance of simultaneous changes in five quantitative parameters known to be important for the detection and assessment of acute MI. In Part 2 of the study, the use of gray-scale Z scores revealed, in each of three subgroups, the distributions of subjects who met ECG criteria for inferior and anterior MI, respectively. Analyzing the Z scores to calculate diagnostic performances yielded results similar to those obtained using receiver-operating characteristic curves of the raw data. We conclude that the use of gray-scale Z scores is a highly efficient and statistically meaningful way to display diagnostic data produced by both continuous and individual recordings.