Diagnosis of nasopharyngeal carcinoma (NPC) at an early disease stage is important for successful treatment and improving the outcome of patients. The use of serum protein profiles and a classification tree algorithm were explored to distinguish NPC from noncancer. Serum samples were applied to metal affinity protein chips to generate mass spectra by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Protein peak identification and clustering were performed using the Biomarker Wizard software. Proteomic spectra of serum samples from 50 NPC patients and 54 noncancer controls were used as a training set and a classification tree with 6 distinct protein masses was generated by using Biomarker Pattern software. The validity of the classification tree was then challenged with a blind test set including another 20 NPC patients and 25 noncancer controls. The software identified an average of 93 mass peaks/spectrum and 6 of the identified peaks were used to construct the classification tree. The classification tree correctly determined 83% (123 of 149) of the test samples with 83% (58 of 70) of the NPC samples and 82% (65 of 79) of the noncancer samples. In a combination of the serum protein profiles with Epstein-Barr (EBV) nuclear antigen 1 (EBNA1 IgA) test, the diagnostic sensitivity and specificity were increased to 99% and 96%, respectively. The results suggest that SELDI-TOF-MS serum protein profiles could discriminate NPC from noncancer. The combination of serum protein profiles with an EBV antibody serology test could further improve the accuracy of NPC screening.