High-throughput microarray experiments often generate far more biological information than is required to test the experimental hypotheses. Many microarray analyses are considered finished after differential expression and additional analyses are typically not performed, leaving untapped biological information left undiscovered. This is especially true if the microarray experiment is from an ecological study of multiple populations. Comparisons across populations may also contain important genomic polymorphisms, and a subset of these polymorphisms may be identified with microarrays using techniques for the detection of single feature polymorphisms (SFP). SFPs are differences in microarray probe level intensities caused by genetic polymorphisms such as single-nucleotide polymorphisms and small insertions/deletions and not expression differences. In this study, we provide a new algorithm for the detection of SFPs, evaluate the algorithm using existing data from two publicly available Affymetrix Barley (Hordeum vulgare) microarray data sets and compare them to two previously published SFP detection algorithms. Results show that our algorithm provides more consistent and sensitive calling of SFPs with a lower false discovery rate. Simultaneous analysis of SFPs and differential expression is a low-cost method for the enhanced analysis of microarray data, enabling additional biological inferences to be made.