Abstract: A method for classification of a target object having a periphery comprises the steps of: selecting a plurality of random first chords D1 across the periphery of the target object; measuring each of the first chords D1 to obtain a plurality of first dimensions; computing for each of the first chords D1 a second chord D2 across the periphery thus forming a plurality of D1, D2 pairs of chords; measuring the second chords D2 for all pairs to obtain second dimensions; computing for each of the pairs of first chords D1 and second chords D2 the ratio D1/D2 of the first dimension to the second dimension to obtain a plurality of first values; computing the logarithm of the first values to obtain a plurality of logarithmic values; computing difference values by subtracting the second dimension from the first dimension for each of the pairs; recording the first values, logarithmic values and difference values in histograms; extracting a vertex lists from the histograms; combining one or more of the vertex lists t

Abstract: A plurality of image chips (202) (over 100), each of the chips containing the same, known target of interest, such as, for example an M109 tank are presented to the system for training. Each image chip of the known target is slightly different than the next, showing the known target at different aspect angles and rotation with respect to the moving platform acquiring the image chip. The system extract multiple features of the known target from the plurality of image chips (202) presented for storage and analysis, or training. These features distinguish a known target of interest from the nearest similar target to the M109 tank, for example a Caterpillar D7 bulldozer. These features are stored for use during unknown target identification. When an unknown target chip is presented, the recognition algorithm relies on the features stored during training to attempt to identify the target.