Next generation imaging spectrometers with higher signal-to-noise ratio and broader swath-width bring new perspectives for crop classification over large areas. Here, we used Hyperion/Earth Observing-One data collected over Brazilian soybean fields to evaluate the performance of four classification techniques (maximum likelihood — ML; spectral angle mapper — SAM; spectral information divergence — SID; support vector machine — SVM) to discriminate five soybean varieties. The spectral resolution influence on classifying them was analyzed by simulating the spectral bands of seven multispectral sensors using Hyperion data. Before classification, the Waikato environment for knowledge analysis was used for feature selection. Results showed the importance of the green, red-edge, near-infrared, and shortwave infrared to discriminate the soybean varieties. Because the soybean variety Monsoy 8411 was sensed by Hyperion in a later reproductive stage, it was more easily discriminated than the other varieties. The best classification techniques were ML and SVM with overall accuracy of 89.80% and 81.76%, respectively. The accuracy of spectral matching techniques was lower (70.84% for SAM and 72.20% for SID). When ML was applied to the simulated spectral resolution of the multispectral sensors, moderate resolution imaging spectroradiometer and enhanced thematic mapper plus presented the highest accuracy, whereas advanced very high resolution radiometer showed the lowest one.