To assess overall survival (OS) of head and neck squamous cell carcinoma (HNSCC) patients and the radiomics features, a large number of quantitative radiomics features were extracted from MRI and selected by machine learning methods. Based on these features, a multivariate Cox proportional hazards model was built as a independent predictor to identify patients. Seven features was found to have association with OS (training cohort, P < 0.0001; testing cohort, P = 0.0013). In the training cohort, the radiomics signature yielded a C-index of 0.73 (95% CI, 0.63-0.84), which was 0.71 (95% CI: 0.59-0.82) in the testing cohort. The potential association between MRI-based radiomics signature and OS was explored.