This chapter aimed to apply a relatively recent diagnostic classification model (DCM) to language test data. The Hierarchical Diagnostic Classification Model (HDCM) is an extension of the generalized deterministic input, noisy, and gate (G-DINA) model. The G-DINA and all the constrained models derived from it assume conjunctive/disjunctive relationships among the attributes required to perform successfully on any given test. However, in instructional settings, the sequence of the presentation of the materials may be reflected in the test takers' responses. Thus, an appropriate model such as HDCM could properly model relationships among the attributes in a context like this. In the present chapter, a brief introduction to DCMs and key considerations in their applications are presented. The readers are walked through the procedures of qualitative and empirical Q-matrix development and validation. Then, the HDCM is applied to language test data. Having read the present chapter, the audience would be able to design a HDCM, evaluate its fit, and interpret the results.