Important similarities and differences are known to exist between the von Mises and wrapped normal distributions, two of the principal models for circular data. In this paper, we consider likelihood-based approaches to determining the sample size required in order to reliably discriminate between the two models. We make use of three new misclassification probability-based criteria to establish lower and upper bounds for the sample size.