This paper presents a method for touch-based gesture recognition that can be used in human-centered interfaces for ambient intelligence applications. Gestures are associated with shapes and they are represented using Fourier coefficients. Neural Networks, Decision Trees, Naive Bayes and a set of classifiers (based on Linear Discriminant Analysis) are tested for gesture recognition. All these methods are evaluated based on accuracy, training and recognition time. Also an application controlled by user using touch-based gestures is implemented.