This paper describes an extension of the n-gram language model: the similar n-gram language model. The estimation of the probability P(s) of a string s by the classical model of order n is computed using statistics of occurrences of the last n words of the string in the corpus, whereas the proposed model further uses all the strings s' for which the Levenshtein distance to s is smaller than a given threshold. The similarity between s and each string s' is estimated using co-occurrence statistics. The new P(s) is approximated by smoothing all the similar n-gram probabilities with a regression technique. A slight but statistically significant decrease in the word error rate is obtained on a state-of-the-art automatic speech recognition system when the similar n-gram language model is interpolated linearly with the n-gram model.