Modem radar performs target recognition and target imaging tasks, in addition to conventional tasks of detection and tracking. New processing techniques, like stepped frequency wave-forms and RE hardware are now becoming available and will soon result in lower-cost high resolution radar for commercial as well as military applications. Advantage ofwide band operation allows generation of synthetic range with resolution of few centimeters required for target identification. An important class ofwave-forms used for high resolution mapping and target imaging, falls under the category called stretch wave-form processing. The simplest wave-form processing uses Fourier transform (FFT or IFFT). Range profiles thus generated, show the scattering centers of the target, and are being used for one—dimensional target identification procedures. These range profiles, however are very sensitive to target registration due to zero sampling inherent in the FFT procedure. This phenomenon together with the well known aspect sensitivity of the target profiles, plays havoc in the automatic target recognition procedures. In this paper we present a completely new method of obtaining range profiles. These profiles do not sample zeros and are robust with respect to range motion or range registration. Based on the super-resolution techniques, analysis is given for the sequential transform procedures. It is shown that all the peaks of the range profiles are preserved and non of the zeros are sampled. The equivalence of the present procedure to Rayleigh's Quotient is discussed. The procedure is then applied to a large set of signatures obtained using electro-magnetic code using high fidelity facet models. The range profiles were generated with the above mentioned procedures and it was found that even though there is sensitivity with respect to the aspect of the targets, the location of scattering centers remain nearly invariant for the limited aspects of the range profiles. We have designed a high dimension Bayesian classifier for the multi-class problem with empirically obtained threshold levels. The statistical separability of different classes was checked with Bhattachariyya distance for various signal to noise ratios. The dassification produces a confusion matrix and Baye's error that are close to theoretical errors for an acceptable level of signal-to-noise ratio. Results are extremely encouraging and the procedure will be extended as applied to real data.