This paper compares the gene expression profiles identified by short (Affymetrix U95AV2) or long (Agilent Hu1A) oligonucleotide arrays on a model for upregulation of a cluster of antioxidant responsive element-driven genes by treatment with tert-butylhydroquinone. MAS5.0, dCHIP, and RMA were applied to normalize the Affymetrix data while Lowess Regression was considered for Agilent data. SAM was used to identify the differential gene expression. A set of biological markers and housekeeping genes were chosen to evaluate the performance of multiple normalization approaches. Both arrays illustrated a definite set of overlapping genes between the datasets regardless of data mining tools used. However, unique gene expression profiles based on the platform used also were revealed and confirmed by quantitative RT-PCR. Further analysis of the data revealed by alternative approaches suggested that alternative splicing, multiple vs. single probe(s) measurement, and use or nonuse of mismatch probes may account for the discrepant data. Therefore, these two microarray technologies offer relatively reliable data. Integration of the gene expression profiles from different array platforms may not only help for cross validation but also provide a more complete view of the transcriptional scenario.