When test measurements of items differ from the true product values because of random measurement error it is possible to reject satisfactory items and to accept inferior ones. Confidence bounds for these misclassification probabilities are obtained, assuming that the true product values and measurement errors are independently distributed normal variates. Both joint and conditional probabilities are investigated. These probabilities are functions of the proportion of measurement error variability (relative to the total variance). Methods are presented for situations where this proportion is known or is estimated from sample data. Tables are provided to simplify the computations required.