Microarray technology has revolutionized molecular biology. The challenge associated with this high-throughput technology is how to analyze and make biological sense of a large amount of microarray data. We introduce AffyMiner, a tool developed for detecting differentially expressed genes from Affymetrix GeneChip microarray data and connecting gene annotation and gene ontology information with the genes detected. AffyMiner consists of three functional modules: GeneFinder for finding significant genes in a treatment versus control experiment; GOTree for mapping genes of interest onto the Gene Ontology (GO) space; and interfaces for running Cluster, a program for clustering analysis, and GenMAPP, a program for pathway analysis. AffyMiner effectively deals with multiple replicates in the experiment, provides users the flexibility of choosing different data metrics for finding differentially expressed genes, and is capable of incorporating various gene annotations. AffyMiner has been used for the analysis of GeneChip data described in several publications and has been found to reduce the time and effort needed to compare data from multiple arrays and to interpret the results in terms of gene and cell functions.