In this paper, we compare four classification techniques for classifying texture data of various natural objects found in FLIR images. The techniques compared include Linear Discriminant Analysis, Mean Classifier and two different models of K-Nearest Neighbor methods. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. 2680 samples for twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across the four classifiers on both un-normalized and normalized data. On un-normalized data, the average best recognition rate obtained using a ten fold cross-validation is 96.5%, and on un-normalized data, it is 86.1% with a single nearest neighbor technique.