To overcome the dimensionality curse of hyperspectral data, an investigation has been done on the use of grouping spectral bands, followed by feature level fusion and classifier decision fusion, to develop an automated target recognition (ATR) system for data reduction and enhanced classification. The entire span of spectral bands in the hyperspectral data is subdivided into groups based on performance metrics. Feature extraction is done using supervised methods as well as unsupervised methods. The effects of classification of the lower dimension data by parametric, as well as non-parametric, classifiers are studied. Further, multiclassifiers and decision level fusion using Qualified Majority Voting is applied to the features extracted from each group. The effectiveness of the ATR system is tested using the hyperspectral signatures of a target class, Cogongrass (Imperata Cylindrica), and a non-target class, Johnsongrass (Sorghum halepense). A comparison of target detection accuracies by before and after decision fusion illustrates the effect of the influence of each group on the final decision and the benefits of using decision fusion with multiclassifiers. Hence, the ATR system designed can be used to detect a target class while significantly reducing the dimensionality of the data.