A stereovision system with image processing algorithm was developed to identify weeds and estimate their positions from the field image. The developed algorithm was designed to automatically segment plants against soil background under multiple illuminations, sunny and shady, in the viewing area. The algorithm segmented plants against soil background and identified individual plants in field images with the processing errors of 2.9 % for the stationary images and 2.1 % for the images captured while the vision system was moving. The algorithm estimated three-dimensional (3D) coordinates of plants in images and a reasonable range of the application inaccuracy was expected due to the limitation in sensing resolution and disparity matching of the stereovision. Root mean square (RMS) error of the estimation was from 43.1 mm to 71.8 mm in 3D coordinates, and the correlation between the measured and estimated 3D coordinates reduces the RMS error to 7.1-12.2 mm. Artificial Neural Network (ANN) was used to identify weeds among the detected plants. Normalized patterns of plants were supplied to the ANN for training and validating the network, respectively. The ANN identified 72.6 % of corn plants in the field images. The main sources of the identification error were identified and additional identification criteria were applied to improve the identification rate: the improvement of the identification rates, 92.5 % and 95.1 % were noted. The stereovision system with developed algorithm may provide a unique technique in sensing plants against the soil background in the image under variable outdoor illuminations without any manual process. The developed system may be useful for autonomous machine-vision based field applications i.e. field navigation, plant specific weed control, plant population mapping and field scouting.