Finding an efficient method to detect counterfeit banknotes is imperative. In this study, we propose multiple kernel weighted support vector machine for counterfeit banknote recognition. A variation of SVM in optimizing false alarm rate, called FARSVM, is proposed which provide minimized false negative rate and false positive rate. Each banknote is divided into m ¡Ñ n partitions, and each partition comes with its own kernels. The optimal weight with each kernel matrix in the combination is obtained through the semidefinite programming (SDP) learning method. The amount of time and space required by the original SDP is very demanding. We focus on this framework and adopt two strategies to reduce the time and space requirements. The first strategy is to assume the non-negativity of kernel weights, and the second strategy is to set the sum of weights equal to 1. Experimental results show that regions with zero kernel weights are easy to imitate with today¡¦s digital imaging technology, and regions with nonzero kernel weights are difficult to imitate. In addition, these results show that the proposed approach outperforms single kernel SVM and standard SVM with SDP on Taiwanese banknotes.