Background: Accurate urinary assays for bladder cancer (BCa) detection would benefit both patients and healthcare systems. Through genomic and proteomic profiling of urine components, we have previously identified a panel of biomarkers that can outperform current urine-based biomarkers for the non-invasive detection of BCa. Herein, we report the diagnostic utility of various multivariate combinations of these biomarkers. Methods: We performed a case-controlled validation study in which voided urines from 550 patients (220 tumor bearing subjects) were analyzed. The urinary concentrations of 14 biomarkers (IL-8, MMP-9,MMP-10, SDC1,CCL18, PAI-1, CD44, VEGF, ANG, CA9,A1AT, OPN, PTX3, and APOE) were assessed by enzyme-linked immunosorbent assay (ELISA). Diagnostic performance of each biomarker and multivariate models were compared using receiver operating characteristic curves and the chi-square test. Results: An 3-biomarker model (CA9, PAI-1 and IL-8) achieved the most accurate BCa diagnosis (sensitivity 90%, specificity 88%). These data show that a multivariate urine-based assay can markedly improve the accuracy of non-invasive BCa detection. Conclusions: Further validation studies are under way to investigate the clinical utility of this panel of biomarkers for BCa diagnosis and disease monitoring.