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In this paper, a toolbox LS-SVMlab for Matlab with implementations for a number of LS-SVM related algorithms is presented. The core of the toolbox is a performant LS-SVM training and simulation environment written in C-code. The functionality for classification, function approximation and unsuperpervised learning problems as well time-series prediction is explained. Extensions of LS-SVMs towards robustness, sparseness and weighted versions, as well as different techniques for tuning of hyper-parameters are included. An implementation of a Bayesian framework is made, allowing probabilistic interpretations, automatic hyperparameter tuning and input selection. The toolbox also contains algorithms of fixed size LS-SVMs which are suitable for handling large data sets. A recent overview on developments in the theory and algorithms of least squares support vector machines to which this LS-SVMlab toolbox is related is presented in [1].