The in situ mRNA hybridization gene expression pattern images of Drosophila melanogaster in the course of embryogenesis provide spatial-temporal information of the expression patterns of a target gene. This information is critical for understanding the roles of genes during the development of embryos. Currently, the annotation of these images is often done by manually assigning a subset of image ontology terms to the images. This approach is time consuming and depends heavily on the consistency

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... of the experts. Alternatively, if the annotation process can be automated or semi-automated, efficiency and consistency are likely to be greatly enhanced. We formulate this task as a pattern classification problem, and present preliminary results. We consider both the template-based global matching and the multiobjective classification based on neural networks (e.g. multi-layer perceptron). We develop a method to combine them to optimize efficiency and accuracy. This method has been applied to the gene expression pattern image database generated by the Berkeley Drosophila Genome Project.