In this paper, we present a neural approach for an unconstrained Arabic manuscript recognition using the online writing signal rather than images. First, we build the database which contains 2800 characters and 4800 words collected from 20 different handwritings. Thereafter, we will perform the pretreatment, feature extraction and classification phases, respectively. The use of a classical neural network methods has been beneficial for the character recognition, but revealed some limitations for the recognition rate of Arabic words. To remedy this, we used a deep learning through the Deep Belief Network (DBN) that resulted in a 97.08% success rate of recognition for Arabic words.