Under Army SBIR Phase I funding, we have developed a nonparametric buried mine classifier using MWIR images. We start with our new image segmentation method based on the wavelet transform. Instead of thresholding the original MWIR images, we first apply the wavelet transform to MWIR image and estimate a threshold value in the corresponding wavelet domain. The small wavelet coefficients are associated with the noise and background clutters appeared in the original image. We then map this threshold in the wavelet domain back to MWIR image domain by applying the inverse wavelet transform. This new threshold is subsequently used to segment the MWIR images and extract small image chips (patches) containing potential buried mines for further detection and classification. In order to perform the statistical classification, we have applied Kolmogorov-Smirnov (KS) test, a powerful nonparametric statistical hypothesis test procedure. One major advantage of using KS test for buried mine detection is that we don't need to make any assumptions of the underlying statistical distributions associated with the cluster intensity variation profiles.