Machinery maintenance accounts for a large proportion of plant operating costs. Compared with the conventional scheduled maintenance strategy which is to stop the machine at pre-determined intervals, modern condition-based maintenance strategy stops the machine only when there is evidence of impending failure. It is now possible to use multi-modal sensor input to monitor machine condition in a collaborative and distributed manner. In this paper, three categories of methods for condition monitoring are reviewed - 1. knowledge based, 2. model based 3. data based methods. Knowledge-based systems are derivations from expert systems that use rules and inference engines to determine failures and their causes. Data-driven methods use machine fault data, typically derived during experiments to train a monitoring system. Pattern recognition algorithms then attempt to classify actual sensor data using the results of the training phase. However it is often impractical to obtain data for every type of fault. Model-based techniques on the other hand use mathematical models to predict machine performance. We propose to combine the model based and data based method for machine condition monitoring. The data is used to train the model and derive its parameters. The various fault modes are then identified and simulated. The output is then input to the classification schemes that can be then used to identify and classify real-time data. We apply the technique to condition monitoring of electrical motors.