Journal bearings are widely used to support the shaft of industrial machinery with heavy loads, such as compressors, turbines and centrifugal pumps. The major problem in journal bearing is catastrophic failure due to corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The method of vibration signal processing using filter analysis for condition monitoring and fault diagnosis has been evolving at a very rapid rate in recent years. The use of filter technique has proven to be a very powerful and reliable tool for fault detection and its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. In this paper the application of Butterworth filter for processing vibration signal to detect faults in journal bearing is presented. A bearing testing apparatus is used for experimental studies to obtain vibration signal from a healthy bearing and a fault bearing. The signal processing methods using Fast Fourier Transform, enveloped filtered power spectrum based on Butterworth filter method and their implementation are presented. Further applications of Artificial Neural Network (ANN) are investigated as a model for automated fault diagnosis
