Colorectal cancer (CRC) is the third most prevalent (136,830 new cases estimated for 2014) and fatal cancers (50,310 deaths estimated) in the world, according to the American Cancer Society. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable surveillance, screening, and primary prevention of CRC are still lacking. In the present study, we propose a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolic profiling approach for highly sensitive and specific colorectal cancer (CRC) detection using human serum samples. 158 metabolites from 25 metabolic pathways of potential significance were monitored. 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls) were analyzed. We detected 113 metabolites out of the 158 monitored, with 42 of them showing statistical significance between CRC cancer and healthy controls, 48 showing statistical difference between CRC cancer and polyp patients, and 8 between healthy controls and polyp patients. Partial least squares-discriminate analysis (PLS-DA) models established using 13 or 14 metabolites proved to be powerful for distinguishing CRC patients from either healthy controls or polyp patients, respectively. Receiver operator characteristic (ROC) curves generated based on these PLS-DA models gave high sensitivities, good specificities, low false discovery rates, and excellent areas under the curve (0.93 and 0.95 respectively, for differentiating CRC patients from healthy controls or polyp patients). Monte Carlo cross validation (MCCV) was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach. To the best of our knowledge, this is the first time that an LC-MS/MS targeted serum metabolic profiling approach has been applied for comparing CRC patients to healthy controls and polyp patients, and our results demonstrate that a panel of serum metabolites enhanced by clinical factors (age, gender, smoking and alcohol status), can potentially serve as novel disease biomarkers for CRC diagnosis.