In this paper, a novel supervised classification approach called Collateral Representative Subspace Projection Modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, CRSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, Decision Table (DT), Nearest Neighbor (NN), KNN, Support Vector Machine (SVM), 1-NN Best Warping Window DTW, 1-NN DTW with no Warping Window, and the well-known classifier boosting method AdaBoost with SVM.