We present an approach for affine-invariant object/target recognition by iconic recognition of image patches that correspond to object surfaces that are roughly planar. Each surface is recognized separately invariant to its 3D pose, employing novel Affine-Invariant Spectral Signatures (AISSs). The 3D-pose invariant recognition is achieved by correlating the image with a novel configuration of Gabor kernels and extracting local spectral signatures. The local spectral signature of each image patch is then matched against a set of iconic models using multi-dimensional indexing in the frequency domain. Affine-invariance of the signatures is achieved by a new configuration of Gabor kernels with modulation in two orthogonal axes. The proposed configuration of kernels is Cartesian with varying aspect ratios in two orthogonal directions. The kernels are organized in subsets where each subset has a distinct orientation. Each subset spans the entire frequency domain and provides invariance to slant (foreshortening), scale and translation within the region of support of the kernels. The union of differently oriented subsets is utilized to achieve invariance in two additional degrees of freedom, i.e. swing and tilt. Hence, complete affine-invariance is achieved by the proposed set of kernels. The indexing method provides robustness in partial distortion, background clutter, noise, illumination effects and lower image resolution. The localized nature of the Gabor kernels allows independent recognition of adjacent shapes that correspond to object parts which could have different poses. The method yields 100% correct recognition rates in experiments over a wide range of slant, scale, swing, and tilt with a dataset of 26 gray-level and infra-red models, in the presence of noise, clutter and other degradations.