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Image annotation, which labels an image with a set of semantic terms so as to bridge the semantic gap between low level features and high level semantics in visual information retrieval, is generally posed as a classification problem. Recently, multi-label classification has been investigated for image annotation since an image presents rich contents and can be associated with multiple concepts (i.e. labels). In this paper, a parametric mixture model based multi-class multi-labeling approach is proposed to tackle image annotation. Instead of building classifiers to learn individual labels exclusively, we model images with parametric mixture models so that the mixture characteristics of labels can be simultaneously exploited in both training and annotation processes. Our proposed method has been benchmarked with several state-of-the-art methods and achieved promising results.