The introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. Unlike conventional RGB color space, it allows the acquisition of large number of spectral bands within the visible spectrum. This results in a feature vector of size greater than 100. For such high dimensionality problems, pattern recognition techniques suffer from the well-known curse-of-dimensionality problem. The two well known techniques to solve this problem are feature extraction and feature selection. In this paper, a feature selection technique using tabu search with an intermediate-term memory is proposed. The cost of a feature subset is measured by leave-one-out correct-classification rate of a Nearest-Neighbor (1-NN) classifier. Experiments have been carried out on textured multispectral images taken at 16 spectral channels and the results have been compared with a reported classical feature extraction technique.