The objective of this talk is to consider the problem of dissolution similarity from a Bayesian viewpoint. Aspects of statistical modeling, software tools/code will be presented. Various elements of a Bayesian approach will be contrasted with their traditional counter-parts. It will be argued that 1)defining similarity parametrically is essential, 2) defining the inference space is essential 3) confidence set approaches are inherently conservative, 4) risk based decision making requires estimating the probability of similarity, 5) estimating the probability of similarity demands a Bayesian viewpoint, and 6) the Bayesian approach is conceptually simple and surprisingly easy to implement. Example data sets will illustrate the estimation of the posterior probability of similarity of: 2 batches and 2 processes. Both model independent and dependent approaches will be illustrated. Pros and Cons of the proposed approach will be discussed.