Signatures of buried landmines are often difficult to separate from those of clutter objects. Often, shape information is not directly obtainable from the sensors used for landmine detection. The Acoustic Sensing Technology (AST), which uses a Laser Doppler Vibrometer (LDV) that measures the spatial pattern of particle velocity amplitude of the ground surface in a variety of frequency bands, offers a unique look at subsurface phenomena. It directly records shape related information. Generally, after preprocessing the frequency band images in a downward looking LDV system, landmines have fairly regular shapes (roughly circular) over a range of frequencies while clutter tends to exhibit irregular shapes different from those of landmines. Therefore, shape description has the potential to be used in discriminating mines from clutter. Normalized Fourier Descriptors (NFD) are shape parameters independent of size, angular orientation, position, and contour starting conditions. In this paper, the stack of 2D frequency images from the LDV system are preprocessed by a linear combination of order statistics (LOS) filter, thresholding, and 2D and 3D connected labeling. Contours are extracted form the connected components and aggregated to produce evenly spaced boundary points. Two types of Normalized Fourier Descriptors are computed from the outlines. Using images obtained from a standard data collection site, these features are analyzed for their ability to discriminate landmines from background and clutter such as wood and stones. From a standard feature selection procedure, it was found that a very small number of features are required to effectively separate landmines from background and clutter using simple pattern recognition algorithms. Details of the experiments are included.