Quantifying pulsatile secretion from serial hormone-concentration measurements (deconvolution analysis) requires automated, objective and accurate detection of pulse times to ensure valid estimation of secretion and elimination parameters. Lack of validated pulse identification constitutes a major deficiency in the deconvolution field, because individual pulse size and number reflect regulated processes that are critical for the function and response of secretory glands. To evaluate deconvolution pulse-detection accuracy, 4 empirical models of true-positive markers of pituitary (luteinizing hormone, LH) pulses were used: (a) Sprague-Dawley rats had recordings of hypothalamic arcuate-nucleus multiunit electrical activity; (b) ovariectomized ewes underwent sampling of hypothalamo-pituitary gonadotropin-releasing hormone (GnRH pulses); (c) healthy young men were infused with trains of biosynthetic LH pulses after GnRH-receptor blockade; and (d) computer simulations were constructed of pulsatile LH profiles. Outcomes comprised sensitivity, specificity and receiver-operating characteristic curves. Sensitivity and specificity were 0.93 and 0.97, respectively, for combined empirical data in the rat, sheep and human (N = 156 pulses) and 0.94 and 0.92, respectively, for computer simulations (N = 1632 pulses). For simulated data, pulse-set selection by the Akaike information criterion yielded slightly higher sensitivity than by the Bayesian information criterion, and conversely for specificity. False-positives errors occurred primarily at low pulse amplitude, and false-negative errors principally with close pulse proximity. Random variability (noise), sparse sampling and rapid pulse frequency reduced pulse-detection sensitivity more than specificity. We conclude that an objective automated pulse-detection deconvolution procedure has high sensitivity and specificity, thus offering a platform for quantitative neuroendocrine analyses.