This paper describes the influence of different populations on statistical multivariate classification rules and classification results where the word 'population' refers only to the frequency of diagnoses to be expected, the so-called prior probabilities. Using linear regression as a multivariate classification technique and six groups consisting of five pathological conditions and normals as test data, it has been shown: 1. (a) That the population influences to a great extent the selection of the best ECG-VCG measurements for the classification rule. 2. (b) That a mismatch of the populations in the learning and test sets can considerably decrease the number of correct classifications. 3. (c) That a certain correction of the mismatch can be achieved when the prior probabilities in the learning and test sets are known. Further, the paper discusses the change of prior probabilities over the years at the Variety Club Heart Hospital in the University of Minnesota and its effect on the performance of the classification algorithm which has been used.