Grain yield is a trait of economic importance to farmers and agricultural industries. There has been much research at molecular and genetic levels to improve grain yield, but environmental factors can be equally or more important. Drought is a common problem in Texas and other arid and semi-arid regions around the globe, affecting crop production adversely. There is always a need of genotypes that can not only grow and develop but produce high yields in water stress conditions. Corn (Zea mays L.) and sorghum (Sorghum bicolor L.) are two major cereal crops of Texas. To identify physiological characteristics of high yielding and drought tolerant corn and sorghum genotypes, 15 entries of each crop were planted in Uvalde, Texas in 2016 and 2017. Three commercial and 12 experimental hybrids of corn as well as eight hybrids and seven inbred lines of sorghum were tested. Performance was evaluated in full and deficit irrigation regimes through plant height, agronomic canopy and leaf traits, grain composition, and grain yield measurement. A sub-sample of genotypes was also tested for soil-water use and transpiration rates; sorghum was found to absorb water to 100-120 cm of soil depth, while corn was limited to 60-80 cm of soil depth. Corn hybrids REV28HR20 (REV26V21), BH8732VTTP, NP2643GT/Tx777 and GP7169GT/Tx777 and sorghum genotypes ATx631/RTx437, ATx642/RTx437, B.Tx642, and B.Tx623 performed good confirming water efficient behavior. Few other genotypes showed water efficient behavior but contributed more towards vegetative development, thus lowering grain yield. Number of green leaves in corn was negatively correlated with grain yield, while in sorghum positive effect on grain yield was observed. Corn hybrids in 2016 and 2017 and sorghum hybrids in 2017 did not show any significant correlation between grain starch content and grain yield. Corn hybrids showed higher water-use efficiency compared to sorghum in terms of grain yield and aboveground biomass. Linear discriminant analysis showed that leaf thickness, leaf dry matter content, osmotic potential, plant height, and NDVI are the most important predictive traits to focus on in the future for similar research to save resources.