Development of a machine vision device to automatically identify different weed species within a field is needed to design a successful spatially variable herbicide applicator. This study was conducted to develop a computer vision algorithm that can successfully identify a broadleaf weed (velvetleaf, Abutilon theophrasti), a grassy weed (wild proso millet, Panicum miliacem), and corn (Zea mays, L.). Digital images were collected in laboratory and field conditions for all three plant species. Image analysis techniques were used to analyze the possibility of using a combination of size and shape features to produce a classification scheme. Two separate approaches were used to classify the velvetleaf from the wild proso millet and corn, and the wild proso millet from the corn. The first and second invariant central moment of inertia measurements along with plant perimeter were used to separate the velvetleaf from the monocot species. Due to the similar shapes of wild proso millet and corn, we were unable to classify the two species by only using size and shape features. Consequently, a two step approach was utilized. This involved using projected perimeter to determine the age (number of days after emergence) of the plant. By knowing the possible age of the plant, the wild proso millet and corn were classified using a combination of length and circularity. Future research will involve the evaluation of several other image features to determine the best classification scheme. Further data will also be collected from a library of laboratory and field images in order to increase the confidence interval of the classification scheme.