Ovarian carcinoma is a leading cause of gynecologic cancer death in women. Despite treatment, a large number of women with ovarian cancer eventually relapse and die of the disease. Hence, recurrent ovarian cancer continues to be a therapeutic dilemma, possibly a result of the emergence of drug resistance during relapse. Recent advances in expression genomics enable global transcript analysis that leads to molecular classification of cancers and prediction of outcome and treatment response. We did a cDNA microarray examination of the expression profiles of eight primary ovarian cancers stratified into two groups based on their chemotherapeutic response. We applied a voice-speech-pattern recognition algorithm for microarray data analysis and were able to model and predict the response of these patients to chemotherapy from their expression profiles. Hence, gene expression profiling by means of DNA microarray may be applied diagnostically for predicting treatment response in ovarian cancer.