Spelling correction is a fundamental task in text mining. In this study, we assess the real-word error correction model proposed by Mays, Damerau and Mercer and describe several drawbacks of the model. We propose a new variation which focuses on detecting and correcting multiple real-word errors in a sentence, by manipulating a probabilistic context-free grammar to discriminate between items in the search space. We test our approach on the Wall Street Journal corpus and show that it outperforms Hirst and Budanitsky’s WordNet-based method and Wilcox-O’Hearn, Hirst, and Budanitsky’s fixed windows size method.