Discrimination of weeds from crop is the first and important step for variable herbicides application and precise physical weed control. Using a new field imaging spectrometer developed by our group, hyperspectral images in the wavelength range 380-870 nm were taken in the wild for the investigation of crop-weed discrimination. After normalizing the data to reduce or eliminate the influence of varying illuminance, stepwise forward variable selection was employed to select the proper band sets and fisher linear discriminant analysis (LDA) was performed to discriminate crop and weeds. For the case of considering each species as a different class, classification accuracy reached 85% with eight selected bands while for the case of considering overall weed species as a class, classification accuracy was higher than 91% with seven selected bands. In order to develop a low-cost device and system in future, all combinations of two and three bands were evaluated to find the best combinations. The result showed that the best three bands can achieve a performance of 89% comparable to the performance achieved by five bands selected using stepwise selection. The authors also found that “red edge” could afford abundant information in the discrimination of weed and crop.