An improved feature geometry based, low-cost, machine vision software system was developed by conducting a statistical reliability study of a previously developed system, evaluating alternative methods of completing the machine vision process, developing more effective edge linking and corner detection algorithms and implementing these new algorithms into a more efficient and reliable version of the existing machine vision software. The resulting system utilizes the same machine vision hardware that was utilized by the previous machine vision software system. This hardware includes an Apple IIe microcomputer, that serves as the host computer for the machine vision system; a 4K frame buffer, that is used to capture designated areas of the image obtained by the image sensor; a General Electric TN2500 Digital Camera, that serves as the sensor; a monitor and a Microbot robotic arm that is used to pick up and move an object for demonstration purposes. The resulting system is capable of recognizing a flat surfaced, randomly positioned rectangular object that contrasts well with its background within about 11 seconds with an overall reliability of about 80%. This system is equipped with an interactive user interface that allows the system to be operated and maintained by a person with very little knowledge of computers and machine vision. The interface gives the user the capability of changing parameters that affect how the machine vision system operates. These features combined with the system’s ability to interactively output the data that is generated during the system’s operation makes this system a valuable teaching tool to demonstrate the application of machine vision to students.