Least squares twin support vector machine (LSTSVM) is a relatively new version of support vector machine (SVM) based on non-parallel twin hyperplanes. Although, LSTSVM is an extremely efficient and fast algorithm for binary classification, its parameters depend on the nature of the problem. Problem dependent parameters make the process of tuning the algorithm with best values for parameters very difficult, which affects the accuracy of the algorithm. The goal of this paper is to improve the accuracy of the LSTSVM algorithm by hybridizing it with simulated annealing.