Core Ideas: Accurate yield prediction is needed for effective sensor‐based N management. Field testing is needed to develop reliable algorithms for silage and grain corn. For the most accurate yield prediction, crop sensing should be done at V6 or later. Predictions for corn silage were more accurate than for corn grain. The use of in‐season‐estimated yield is preferred across variable sites. Crop sensing is a promising approach for predicting corn (Zea mays L.) yield. Yield prediction is the first step in development of algorithms for sensor‐based N management. Here, we evaluated the impact of (i) timing of sensing (growth stage), and (ii) method of reporting sensor data on estimations of corn grain and silage yield in New York. Sensor data were reported as the normalized difference vegetation index (NDVI), in‐season estimated yield (INSEY) expressed as NDVI divided by days after planting (DAP; INSEY_DAP), growing degree days (GGD; INSEY_GGD), and the inverse simple ratio (ISR; [1–NDVI]/[1+NDVI]) divided by DAP (INSEY_ISR). To evaluate timing of sensing, corn of six fertility trials was scanned at every growth stage between V4 and V11. The replicated trials had up to six N rates (0, 56, 112, 168, 224, and 336 kg ha⁻¹). The V7 sensor and yield data from zero‐N plots of nine additional on‐farm trials (varying histories) were added to derive yield algorithms for New York. Drought at three sites in 2016 negatively impacted the accuracy of sensor‐based grain yield estimates (R² < 0.27). Excluding these sites, most accurate yield predictions were obtained from V6 onward. Across different locations and independent of reporting method, INSEY data at V7 predicted yield with an R² > 0.70 (grain) and >0.77 (silage). We conclude that INSEY data obtained at V7 can be used to accurately predict corn grain and silage yields in non‐drought conditions in New York.