We consider here how to assess if two classifiers, based on a set of test error results, are performing equally well. This question is often considered in the realm of sampling theory, based on classical hypothesis testing. Here we present a simple Bayesian treatment that is quite general, and also is able to deal with the (practically common) case where the errors that two classifiers make are dependent.