Soil C sequestration (SCS) has the potential to attenuate increasing atmospheric CO₂ and mitigate greenhouse warming. Understanding of this potential and complex soil C process is aided by the use of simulation models. We evaluated the ability of the EPIC model to simulate corn (Zea mays L.) yields and soil organic C (SOC) at Arlington, WI during 1958-1991. Corn was grown continuously on a Typic Argiudoll with 3 N levels:  LTN1 (control), LTN2 (medium), and LTN3 (high). The LTN2 N rate started at 56 kg ha^-1 (1958), increased to 92 kg ha^-1 (1963), and reached 140 kg ha^-1 (1973). The LTN3 N rate was maintained at twice the LTN2 level. In 1984, each plot was divided into 4 subplots receiving N at 0, 84, 168 and 252 kg ha^-1. Five treatments were used for model evaluation. Percent errors of mean yield predictions during 1958-1983 decreased as N rate increased (LTN1 = -5.0%, LTN2 = 3.5%, and LTN3 = 1.0%). Percent errors of mean yield predictions during 1985-1991 were larger than during the first period. Simulated and observed mean yields during 1958-1991 were highly correlated (R² = 0.961**). Simulated SOC agreed well with observed values with percent errors from -5.8% to 0.5% in 1984 and from -5.1% to 0.7% in 1990. EPIC captured the dynamics of SOC, SCS, and microbial biomass. Simulated net N mineralization rates were lower than those from lab incubations. Improvements in EPIC’s ability to predict annual variability of crop yields may lead to improved estimates of SCS.