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Classical clustering methods do not provide users with direct control of theclustering results, and the clustering results may not be consistent with therelevant criterion that a user has in mind. In this work, we present a newmethodology for performing image clustering based on user-specified textcriteria by leveraging modern vision-language models and large language models.We call our method Image Clustering Conditioned on Text Criteria (IC|TC), andit represents a different paradigm of image clustering. IC|TC requires aminimal and practical degree of human intervention and grants the usersignificant control over the clustering results in return. Our experiments showthat IC|TC can effectively cluster images with various criteria, such as humanaction, physical location, or the person's mood, while significantlyoutperforming baselines.