This paper presents an approach to automatic assessment on articulation disorders using unsupervised acoustic model adaptation. Prior knowledge is obtained via the phonological analysis of the speech data from 453 articulation disordered children. A confusion matrix of the recognition units for a specific subject is re-estimated based on the prior knowledge and the recognition results to choose the confident units for adaptation. The adapted acoustic models can effectively improve the recognition performance of the disordered speech and thus used for articulation assessment. In the experiments, the proposed unsupervised adaptation method achieved a significant performance improvement of 9.1% for disordered speech on syllable recognition rate. Automatic assessment also shows encouraging consistency to the assessment from the therapist.