Cognitive Diagnostic Models are a class of multidimensional categorical latent trait models which provide diagnostic information by reporting examinees' mastery profiles on a set of predefined skills. CDMs provide fine grained information concerning examinees' strengths and weaknesses in the subskills and subprocesses which constitute a larger domain of knowledge. Such detailed information helps in classroom teaching, designing remedial courses, and material development. In this study, we analysed a high stakes English as a foreign language reading comprehension test using GDINA model. The skill profiles of the test takers, the class probabilities, attribute mastery probabilities, attribute difficulties, and model data fit at test and item level were examined. Implications of the study for reading comprehension research and CDM applications are discussed.