We describe an algorithm that relies on web frequency counts to identify and correct writing errors made by non-native writers of English. Evaluation of the system on a real-world ESL corpus showed very promising performance on the very difficult problem of critiquing English determiner use: 62% precision and 41% recall, with a false flag rate of only 2% (compared to a random-guessing baseline of 5% precision, 7% recall, and more than 80% false flag rate). Performance on collocation errors was less good, suggesting that a web-based approach should be combined with local linguistic resources to achieve both effectiveness and efficiency.