In this work we propose an algorithm for Peruvian counterfeit banknotes detection. Our algorithm operates in banknotes with 50, 100 and 200 soles denominations that were manufactured from 2009 onwards. This algorithm offers an automatic diagnosis based on digital image processing and support vector machines (SVM). Current Peruvian counterfeit detection systems are specially designed to analyze relevant characteristics in dollars and euros. Then, some counterfeiters can fool these systems. We made our detection system robust because we focus on the image acquisition and the segmentation of intaglio marks engraved over the banknotes. After segmentation, we applied embossing and Sobel filters followed by an aperture morphological operation to obtain special characteristics that were then classified by an SVM. We have validated our methodology using real and fake banknotes from a dataset of 240 samples provided by Central Reserve Bank of Peru (BCRP). Our final identification accuracy was 96.5%.