The speed and parallel operations capabilities of optical data processors make them ideally suited to the automatic target identification problem. However, to be successful, a means for analyzing the correlations must be provided, thus a hybrid processor is implied. A system which utilizes both optical correlation and Euclidean distance classification for target identification will be detailed. Both operations are necessary here for successful target recognition because optical correlation in general yields results that vary slowly with parameter changes. These results are then supported by further signature classification to create the sensitivity that is otherwise lacking. This concept is independent of the way in which the data is acquired but simply utilizes the input data to realize a multiple feature extraction of the target. Optical correlation is performed between the acquired, multiplexed features and ideal (e.g. aircraft) features. This then produces a multi-point target signature in feature space which is recognized and identified by the "designator", a hard wired Euclidean distance classifier. Already stored in the designator are the signatures generated from the ideal feature correlation masks used to train and load the device. The designator is a hybrid computer; it stores up to 200 aim points in eight dimensions with class names, and sequentially tests these against an eight-channel analog input. It computes each distance and determines if this is smaller than the previous value in 10 microseconds and completes the entire analysis in 2 1/2 milliseconds. The results obtained are the names of the closest and the next closest designated class. Results of experiments performed using the designator for cloud detection and screening will be shown.