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In this paper, we address the problem of detecting multiple topics or categories of text where each text is not assumed to belong to one of a number of mutually exclusive categories. Conventionally, the binary classification approach has been employed, in which whether or not text belongs to a category is judged by the binary classifier for every category. In this paper, we propose a more sophisticated approach to simultaneously detect multiple categories of text using parametric mixture models (PMMs), newly presented in this paper. PMMs are probabilistic generative models for text that has multiple categories. Our PMMs are essentially different from the conventional mixture of multinomial distributions in the sense that in the former several basis multinomial parameters are mixed in the parameter space, while in the latter several multinomial components are mixed. We derive efficient learning algorithms for PMMs within the framework of the maximum a posteriori estimate. We also empirically show that our method can outperform the conventional binary approach when applied to multitopic detection of World Wide Web pages, focusing on those from the "yahoo.com" domain.