As learner corpora have presently become readily accessible, it is practicable to examine interlanguage errors and carry out error analysis (EA) on learner-generated texts. The data available in a learner corpus enable researchers to investigate authentic learner errors and their respective frequencies in terms of types and tokens as well as contexts in which they regularly occur. The need to consider these authentic learner errors in the design of useful language learning programs and remedial teaching materials has been widely emphasized by many researchers (see e.g., Juozulynas, 1994; Mitton, 1996; Cowan, Choi, & Kim, 2003; Ndiaye & Vandeventer Faltin, 2003; Allerton et al., 2004). This study aims at analyzing inflectional, derivational and word form errors for verbs produced by Turkish EFL learners across six distinct proficiency levels, A1-A2; B1-B2; C1-C2, as defined by Common European Framework of Reference for Languages (henceforth CEFR) (Council of Europe, 2001). The corpus used in this study is the Cambridge Learner Corpus (CLC), the largest annotated test performance corpora which enables the investigation of the linguistic and rhetorical features of the learner performances in the above stated proficiency bands. The findings from this study seem to indicate that, across different proficiency levels and across different registers and genres, the most common verb error categories are incorrect tense of verb (TV), wrong verb choice (RV), wrong verb form (FV), missing verb (MV), and verb agreement (AGV) errors. This study’s approach uses the techniques of computer corpus linguistics and has its roots in the Error Analysis framework as proposed by Corder (1971): identification, description, classification and explanation of errors.