The occurrence of large publically available repositories of human breast tumor gene expression profiles provides an important resource to discover new breast cancer biomarkers and therapeutic targets. For example, knowledge of the expression of the estrogen and progesterone hormone receptors (ER and PR), and that of the ERBB2 in breast tumor samples enables choice of therapies for the breast cancer patients that express these proteins. Identifying new biomarkers and therapeutic agents affecting the activity of signaling pathways regulated by the hormone receptors or ERBB2 might be accelerated by knowledge of their expression levels in large gene expression profiling data sets. Unfortunately, the status of these receptors is not invariably reported in public databases of breast tumor gene expression profiles. Attempts have been made to employ a single probe set to identify ER, PR and ERBB2 status, but the specificity or sensitivity of their prediction is low. We enquired whether estimation of ER, PR and ERBB2 status of profiled tumor samples could be improved by using multiple probe sets representing these three genes and others with related expression.We used 8 independent datasets of human breast tumor samples to define gene expression signatures comprising 24, 51 and 14 genes predictive of ER, PR and ERBB2 status respectively. These signatures, as demonstrated by sensitivity and specificity measures, reliably identified hormone receptor and ERBB2 expression in breast tumors that had been previously determined using protein and DNA based assays. Our findings demonstrate that gene signatures can be identified which reliably predict the expression status of the estrogen and progesterone hormone receptors and that of ERBB2 in publically available gene expression profiles of breast tumor samples. Using these signatures to query transcript profiles of breast tumor specimens may enable discovery of new biomarkers and therapeutic targets for particular subtypes of breast cancer.