The impact localization in composite panels is assessed using two machine learning techniques: least square support vector machines (LSSVM) and artificial neural networks (ANN) with local strain signals from piezoelectric sensors. Sensor signals from impact experiments on a composite plate as well as signals simulated by a finite element model are used to train and test models. A comparative study shows that LSSVM achieves better accuracy than ANN on identifying location of impacts for a combination of large mass impact and small mass impact, in particular when less data is available for training which is more appropriate for real aeronautical application. Additionally, LSSVM is more capable of identifying new impact events which have not been considered in the training process.