The aim of this study was to determine the applicability of near infrared spectroscopy (NIRS) for the nutritional assessment of two important forage species in the country: alfalfa (Medicago sativa L) and red clover (Trifolium pratense L). For this, 75 samples of alfalfa varieties (SW 8210, WL 625HQ) and 75 of red clover varieties (Quiñequeli, Kendland) obtained from the paddocks of the IVITA El Mantaro Experimental Station, Junín region, Peru were used. Proximal analysis was performed determining the content of crude protein (CP), ether extract (EE), crude fibre (CF), total ash (TA) and neutral detergent fibre (NDF), and the spectrum was captured using NIRS equipment. The calibration and validation models were developed to estimate the predictive capacity using Partial Least Squares (PLS), and the accuracy and precision statistics used were the Correlation Coefficient (R), Determination Coefficient (R2), Root Mean Square Error of Calibration (RMSEC), Root Mean Square of Prediction Error (RMSEP), Ratio of Range to Error (RER) and Residual Predictive Deviation (RPD). The mathematical models obtained showed that the NIRS technique has a good predictive capacity for the nutritional components of CP, TA and NDF (R2: 0.97, 0.99, 0.94; RPD: 2.00, 2.17 and 2.00, respectively) for varieties of alfalfa and red clover.