Turbomachinery condition monitoring and fault detection in the Malaysian oil and gas industry is currently done by monitoring the parameters of the equipment, such as a gas turbine, based on limits provided by the original equipment manufacturer. This is performed in an attempt to avoid any unscheduled downtime and catastrophic failure of the machinery. However, this method has proven to be insufficient and ineffective in providing early information or warning regarding machine faults. This paper presents a case study of a gas turbine that developed a blade fault in an oil and gas plant despite operating within its original equipment manufacturer limits. The parameters used for machinery condition monitoring were then analyzed using a self-organizing map; a two-dimensional graphical layout consists of neurons arranged in contact with one another. The results demonstrate that such a map is efficient in providing early warnings regarding turbomachinery’s health conditions.