Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. We present our results from integrating a Self-Organizing Maps (SOM) and a Support Vector Machine (SVM) for the analysis of the various functions of Zebra fish genes based on their expression. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.