Early detection of cancer is a critical issue for improving patient survival rates. Recent progress in mass spectrometry has shown the promising potential of biomarker discovery in the diagnosis of diseases especially in early stages. In the present study, an alternative approach to feature extraction from mass spectrometry data of prostate cancer is proposed that results in the definition of different biomarkers. The latter provide information rich features that improve the performance of an MLP classifier in differentiating among datasets with different PSA levels of prostate cancer and with no evidence of disease. Prostate cancer dataset was collected from the National Cancer Institute Clinical Proteomics Database. The overall accuracy, in correctly classifying 63 spectra with no evidence of disease (PSA<1) and 69 spectra with prostate cancer (PSA≥4), was 95%. Furthermore 93% was the classification overall accuracy in discriminating 26 spectra of prostate cancer with (4 PSA<10) from 43 spectra of prostate cancer with (PSA>10). The high accuracies obtained by the proposed method might lead to informative biomarkers for early stage of prostate cancer diagnosis.