Affymetrix microarrays measure gene expression based on the intensity of hybridization of a panel of oligonucleotide probes (probe set) with mRNA. The signals from all probes within a probe set are converted into a single measure that represents the expression value of a gene. This step diminishes the number of independently measured parameters and eliminates from consideration individual "good-working" probes. We propose a new feature selection algorithm (Probe Level Universal Search or PLUS algorithm) for probe-level analysis of gene expression datasets. The algorithm evaluates the intensities of perfect-match Affymetrix probes individually and selects probes that allow one to distinguish two given classes of samples. The algorithm was used to differentiate the samples according to their gender ("gender differentiation"). The universal gender differentiating set of 3' Gene Affymetrix microarray probes was selected; the set consists of 38 probes from XIST gene of X-chromosome and 17 probes from five Y-chromosome genes: RPS4Y1, EIF1A, DDX3Y, JARID1D and USP9Y. The selection procedure based on the probes selected by PLUS algorithm differentiates the sex chromosome karyotype of the sample, reveals samples with incorrect gender labels and samples from patients with hereditary syndromes or cancer-associated chromosome abnormalities.