The development of a multivariate diagnostic tool relating corn silage (CS) quality traits and herd productivity or profitability could be helpful for producers. Despite the fact that CS analyses are commonly available, the capacity of the producers to relate the information to herd performance is complicated due to the high number of chemical, biological and fermentative parameters (>20 variables). These parameters could also be affected by harvested years (HY), fermentation length (FL) and farms, making interpretation and decision process complicated for the producers. Thus, our aim was to formulate comprehensive quality parameters for CS that will help producers understand the quality assessment formulated by the analytic report and validated interpretation consistency over different HY, FL and growing regions (GR). A principal component (PC) analysis was performed using SAS 9.4 with 2,124 CS samples harvested from 2014 to 2018 HY in Québec, which were analyzed by near-infrared spectroscopy for nutritional and fermentation characteristics. Six PC were retained and were interpreted based on loading vectors of each variables on specific extracted PC, where loading are the correlation between latent component and original variables. PC corresponds to: PC1) carbohydrates and maturity of CS; PC2) homolactic fermentation, protein solubilization and starch digestibility (DEG); PC3) FL, heterolactic fermentation, or other secondary fermentations; PC4) NDF DEG; PC5) protein degradation and heating; and PC6) metabolites associated with L. buchneri group. The interpretation consistency validation of the PC was performed from a subset (n = 2061) containing GR (n = 7), HY (n = 4) and FL. The statistical model included: farm origin (random effect), HY, GR, FL and the following interactions: HY × GR, HY × FL. The PC were respectively affected (P < 0.05) by: HY, FL (PC2); HY, GR, FL (PC1, PC3, PC4, PC5, and PC6); HY × GR (PC1, PC2, PC4, and PC5); HY × FL (PC3, PC4, PC5, and PC6). The relationship between the PC and HY, GR or FL allows to validate PC interpretation and moving forward to connecting the PC to dairy herd productivity and profitability using farm historical database.