This paper presents a project that deals with online character recognition based on various time series such as acceleration, gyro and force. One important aspect of this work is the comparison and evaluation between a complex neural network-more precisely a Long-Short-Term-Memory network (LSTM)-and more comprehensible models such as K-Nearest Neighbours (KNN), Linear Discriminant Analysis (LDA) and NaiveBayes (NB). LSTMs have shown to not only be suitable for speech recognition but also for the recognition of handwriting based on time series due to its ability to learn long-Time dependencies of the provided data. Online handwriting describes the usage of a digitizer in order to convert analogue sensor data to a digital representation of the handwriting. The digitizer used in our approach refers to a sensor enhanced ball pen developed by STA-BILO. The data set used for training consists of approximately 20,000 lowercase English alphabet characters in total contributed by 15 different people. The discussed approaches are evaluated and compared against each other using the recorded dataset, while taking recognition rates (i.e., accuracy) into account.