Background: The systemic inflammation response index (SIRI) and prognostic nutritional index (PNI) have been shown to be correlated with the prognosis of various solid tumors. This study sought to investigate the prognostic value of the SIRI and the PNI individually and in combination in locally advanced elderly esophageal squamous cell carcinoma (ESCC) patients treated with radical radiotherapy. Methods: The data of 192 ESCC patients aged >= 65 years, who had been treated with definitive radiotherapy between 2013 and 2016, were retrospectively analyzed. The optimal cutoff values of SIRI and PNI were determined by receiver operating characteristic curves. Kaplan-Meier curves and Cox proportional hazards models were used to analyze the effect of the SIRI and PNI on overall survival (OS) and progressionfree survival (PFS). The areas under the curve were measured to evaluate the predictive ability of the SIRI, PNI, and SIRI combined with PNI for OS. Results: The optimal cutoff values of the pretreatment SIRI and PNI were 1.03 and 49.60, respectively. The univariate and multivariate analyses demonstrated that T stage (P=0.021), TNM stage (P=0.022), synchronous chemotherapy (P=0.032), the SIRI (P=0.001), and the PNI (P=0.045) were independent prognostic factors for OS and N stage (P=0.004), synchronous chemotherapy (P=0.016) and the SIRI (P=0.004) were independent prognostic factors for PFS. The AUC of the combined SIRI and PNI (0.706; 0.612-0.801) was higher than those of the SIRI (0.648; 0.540-0.756) and the PNI (0.621; 0.523-0.720). Patients in the low-SIRI and high-PNI groups, especially those in clinical stage II or who received synchronous chemotherapy (P<0.001, P=0.002), had better OS and PFS than those in the other groups Conclusions: The SIRI and PNI are simple and reliable biomarkers for predicting long-term survival in elderly patients with locally advanced ESCC after radical radiotherapy. A high SIRI and a low PNI indicated poor prognosis, and the combination of the SIRI and PNI improved the accuracy of prognosis prediction and could be used to guide individualized treatment of patients.