Independence between individual classifiers is typically viewed as an asset in classifier fusion. We study the limits on the majority vote accuracy when combining dependent classifiers. Q statistics are used to measure the dependence between classifiers. We show that dependent classifiers could offer a dramatic improvement over the individual accuracy. However, the relationship between dependency and accuracy of the pool is ambivalent. A synthetic experiment demonstrates the intuitive result that; in general, negative dependence is preferable.