Improvements in the quality of gene expression data were investigated based on a database consisting of 5168 oligonucleotide microarrays collected over 3 years. The database includes diverse treatments of human and mouse samples collected from multiple laboratories. The array designs and algorithms used to capture the data have also changed over the 3 years of data collection. All hybridizations and labeling were conducted in the Hartwell Center for Bioinformatics and Biotechnology at St. Jude's Children's Research Hospital. Quality metrics for each human and mouse array were collected and analyzed. Statistical tests, such as ANOVA and linear regression, were applied to test for the effects of array design, algorithm, and time. The quality metrics tested were average background, actin 3'/5' ratio, Bio B signal, percent present, and scale factor. ANOVA results indicate that both recent algorithms and chip designs significantly correlate with improvements in Bio B, scale factor, percent present, and average background. Significant quality improvements correlated with new chip designs, algorithms, and their interaction. In addition, within one chip type analyzed by the same algorithm significant improving trends were still observed. Scale factor, percent present, and average background significantly improved over time for U133A arrays analyzed by the Affymetrix MicroArray Suite 5.0 algorithm according to linear regression. Proportionally fewer outlier arrays (those with less than 25% present calls) were seen over time. Also, high throughput periods did not increase the proportion of outliers, indicating that laboratory monitoring of quality is successfully preventing failures.