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I-vector based recognition is a well-established technique in stateof- the-art speaker and language recognition but its use in dialect and accent classification has received less attention. We represent an experimental study of i-vector based dialect classification, with a special focus on foreign accent detection from spoken Finnish. Using the CallFriend corpus, we first study how recognition accuracy is affected by the choices of various i-vector system parameters, such as the number of Gaussians, i-vector dimensionality and reduction method. We then apply the same methods on the Finnish national foreign language certificate (FSD) corpus and compare the results to traditional Gaussian mixture model - universal background model (GMM-UBM) recognizer. The results, in terms of equal error rate, indicate that i-vectors outperform GMM-UBM as one expects. We also notice that in foreign accent detection, 7 out of 9 accents were more accurately detected by Gaussian scoring than by cosine scoring.