By entering the post-genome era, it becomes more important to determine relationships of genes in expression in order to understand the roles of genes in a genome. The Microarray is one of the important methods to identify expression patterns and interactions between genes on the genomic scale. The gene expression data from microarray analysis have been increased exponentially. However, handling hundreds of microarray data sets all together is complex and troublesome, and most of the biologists not familiar with computer work have difficulties in dealing with massive amount of microarray data. Furthermore, each microarray data set usually has different level of signal intensity and background noise from others, depending on experimental conditions even done with same sample. Therefore, in order to determine general relationship between genes using data sets obtained under various experiments, additional processing of microarray data sets is needed, such as normalization and statistical analysis. Here, we built a database named PlantArrayNet (PAN), which can analyze hundreds of gene expression profiles, to get relations between genes in expression. PAN provides correlating information between genes through correlational network and phylogenetic tree which is based on the correlation levels between genes evaluated by Pearson’s r-value. PAN also provides scatter plot of log ratio intensities between genes, related pathway maps and cis-element list of promoter regions. Currently, PAN provides correlating information of Oryza sativa, Arabidopsis thaliana and Brassica rapa. PAN could be used as a useful tool for understanding of expressional relation between genes and roles of genes in a genome.