Automatic pronunciation scoring makes novel applications for computer assisted language learning possible. In this paper we concentrate on the feature extraction. A relatively large feature vector with 28 sentence- and 33 word-level features has been designed. On the word-level correctly and mispronounced words are classified, on the sentence-level utterances are rated with 5 discrete marks. The features are evaluated on two databases with non-native adults’ and children’s speech, respectively. Up to 72 % class-wise-averaged recognition rate is achieved for 2 classes; the result of the 5-class problem can be interpreted as 80 % recognition rate.