In this paper, we propose two methods for automatically obtaining hypotheses about pronunciation variation. To this end, we used two different approaches in which we employed a continuous speech recognizer to derive this information from the speech signal. For the first method, the output of a phone recognition was compared to a reference transcription in order obtain hypotheses about pronunciation variation. Since phone recognition contains errors, we used forced recognition in order to exclude unreliable hypotheses. For the second method, forced recognition was also used, but the hypotheses about the deletion of phones were not constrained beforehand. This was achieved by allowing each phone to be deleted. After forced recognition, we selected the most frequently applied rules as the set of deletion rules. Since previous research showed that forced recognition is a reliable tool for testing hypotheses about pronunciation variation, we can expect that this will also hold for the hypotheses about pronunciation variation which we found using each of the two methods. Another reason for expecting the rule hypotheses to be reliable is that we found that 37-53% of the rules are related to Dutch phonological processes that have been described in the literature.