When performing a computer task, a user will decompose the task into cognitive goals and subgoals. These goals are accomplished through the use of external operators (e.g. keystrokes, mouse button presses) or internal mental operators (e.g. reading parts of the display, deciding on the goal). Users may utilize different goals and sequence the goals differently to accomplish the same overall task. Determining the goals and the sequencing of the goals could be useful for several reasons, such as providing a means for on-line assistance with the task. Determining these goals in the past, however, has been a time-consuming process. A neural network tool for automatically identifying cognitive text-editing goals from operators is investigated. The first of three memos edited by subjects was used to train the neural network successfully to map the operators (keystrokes) to cognitive goals. In a test of the trained network's ability to generalize to new input-the second and third memos edited by the subjects-the net could identify the cognitive goals with an overall performance accuracy of (96 %). Two methods were used to investigate the validity of the goals which were identified by the tool. The characteristics of the goals were consistent with that which could be expected based upon previous research. This research illustrates that a neural network tool can identify the cognitive goals of a lask.