Bayesian methodology is a coherent process for building prior distributions based on historical data. Using this data can increase the power of clinical trials while keeping sample size the same. However, there are a number of approaches to do this with no clear "best" way to incorporate the prior knowledge. For example, simple pooling may follow directly from Bayes Theorem but can lead to upward bias when applied to clinical trials. In this talk we will review a number of useful approaches of using historical controls with example illustrations. In particular, we will examine hierarchical models, mixture priors, and variants of power priors. Practical considerations as well as impact on type I and type II errors will be discussed.