Recycled paper is extensively used worldwide. In the last decades, its market has expanded considerably. The increasing use of recycled paper in papermaking has led to the production of paper containing several types of impurities. Consequently, wastepaper mills are forced to implement quality control schemes for evaluating the incoming wastepaper stock, thus guarantying the specifications of the final product. The main objective of this work is to present a fast and reliable system for identifying different paper types. Therefore, undesirable paper types can be refused, improving the performance of the paper machine and the final quality of the paper manufactured. For this purpose, two fast techniques, i.e., Fourier transform midinfrared (FTIR) and reflectance near infrared (NIR), were applied to acquire the infrared spectra of the paper samples. Next, four processing multivariate methods, i.e., principal component analysis, canonical variate analysis (CVA), extended CVA (ECVA), and support vector machines (SVMs), were employed in the feature-extraction or dimension-reduction stage. Afterward, the k nearest neighbor (k NN) algorithm was used in the classification phase. Experimental results show the usefulness of the proposed methodology and the potential of both FTIR and NIR spectroscopic methods. Using the FTIR spectrum in association with SVM and kNN, the system achieved a maximum classification accuracy of 100%, whereas using the NIR spectrum in association with ECVA or SVM and kNN, the system achieved a maximum classification accuracy of 96.4%.