In this paper we exploit the use of synthetic data for on-line handwritten gesture commands recognition with an emphasis on the problem of forgetting unused classes. For on-line learning, one of the most crucial moments of the processing is the initialization. In some applications the data is available and these can be fed to the learning model. However, in applications such as user-friendly handwritten gesture recognition, this scenario is not possible. Since from the user perspective it is better to let the user define his own symbols, the learning model is lacking in the amount of data at the initialization. Some strategies have been proposed to acquire synthetic handwritten gesture commands and use these for on-line learning. In this paper we exploit this technique further and focus on the forgetting of unused classes by applying a random buffer and Elastic Memory Learning (EML) to avoid this from happening. In the experiments we search for the proper amount of synthetic data produced for each sample as well as exploit the most appropriate time to stop the generation of synthetic data for learning purposes. We also investigate the influence of synthetic data on forgetting when using the proposed EML. We base the generation of synthetic gesture commands on Kinematic Theory.