Diagnostic language assessment can greatly benefit from a collaborative union of computer-assisted language testing (CALT) and natural language processing (NLP). Currently, most CALT applications mainly allow for inferences about L2 proficiency based on learners’ recognition and comprehension of linguistic input and hardly concern language production (Holland, Maisano, Alderks, & Martin, 1993). NLP is now at a stage where it can be used or adapted for diagnostic testing of learner production skills. This paper explores the viability of NLP techniques for the diagnosis of L2 writing by analyzing the state of the art in current diagnostic language testing, reviewing the existing automated scoring applications, and considering the NLP and statistical approaches that appear promising for automated diagnostic writing assessment for ESL learners.