Statistical testing provides a tool for engineers and operators to judge the validity of process measurements and data reconciliation. Univariate, maximum power and chi-square tests have been widely used for this purpose. Their performance, however has not always been satisfactory. A new class of test statistics for detection and identification of gross errors is presented based on principal component analysis and is compared to the other statistics. It is shown that the new test is capable of detecting gross errors of small magnitudes and has substantial power to correctly identify the variables in error, when the other tests fail.