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Language sample analysis is an important technique used in measuring language development. At present, measures of grammatical complexity such as the Index of Productive Syntax (Scarborough, 1990) are used to measure language development in early childhood. Although these measures depict the overall competence in the usage of language, they do not provide for an analysis of the grammatical mistakes made by the child. In this paper, we explore the use of existing Natural Language Processing (NLP) techniques to provide an insight into the processing of child language transcripts and challenges in automatic grammar checking. We explore the automatic detection of 6 types of verb related grammatical errors. We compare rule based systems to statistical systems and investigate the use of different features. We found the statistical systems performed better than the rule based systems for most of the error categories.