Independent component analysis (ICA) for convolutive mixtures is often applied in the frequency domain due to the desirable decoupling into independent instantaneous mixtures per frequency bin. This approach suffers from a well-known scaling and permutation ambiguity. Existingmethods perform a computation-heavy and sometimes unreliable phase of post-processing which typically makes use of knowledge regarding the geometry of the sensors post-ICA. In this paper, we propose a natural way to incorporate a priori knowledge of the unmixing matrix in the form of a prior distribution. This softly constrains ICA in a manner that avoids the permutation problem, and also allows us to integrate information about the environment, such as likely user configurations, into ICA using a unified statistical framework. Maximum a priori ICA easily follows from the maximum likelihood derivation of ICA. Its effectiveness is demonstrated through a series of experiments on convolutive mixtures of speech signals.