In this thesis results are presented which relate to using artificial neural networks to predict the onset of Parkinson's disease tremors in human subjects. Data for the networks was obtained from implanted deep brain electrodes in human subjects. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings. Parkinson's disease (PO) is one disease in a group of conditions called movement disorders. One of the primary symptoms of Parkinson's disease is tremor, and in the extreme case, the patient can suffer loss of physical movement. There are two major types of treatment for PO currently available, namely chemical treatment (Levodopa) and surgical implants (Deep Brain Stimulation). Deep Brain Stimulation (DBS) has been widely accepted as an efficient treatment for PO over the past decade. Despite the high cost of surgical operation, deep brain stimulation has become a widely accepted alternative choice (if not the only) to medical treatment such as Levodopa for patients. In this work, number of methods have been applied on exploring the possibility of determining PO tremor onset from patient's brain signal, in particular using combination of artificial neural networks (ANN) and advanced signal processing algorithms. The result of this work could eventually lead to design a deep brain stimulation device with the ability to react on different brain activities, for example, start stimulation just before Parkinson's disease tremor onset. The benefits of such smart device are pre-Iong DBS battery life and reduce stimulation interference on normal brain functions.