Vibration is one of the major parameters to consider in condition monitoring of rotating systems. If an undetected fault is noticed in the rotating system, then, at best, the issue will not be too significant and can be remedied cheaply and quickly; at worst case, it may result in down-time, expensive damage, injury, or even life loss, therefore early fault identification is a critical factor in ensuring and extending the working life of the rotating systems. By measurement and analysis of the vibration of rotating machinery, it is possible to detect and locate important faults such as mass unbalance, misalignment, bearing failure, gear faults and rotor cracks. This article is aimed to guide the researchers to implement identification, diagnosis and remedy techniques of common fault types using vibration analysis and outlines many important techniques used for condition monitoring of rotating systems such as fast Fourier transform, frequency domain decomposition method, wavelet transform, stochastic subspace identification and deep learning.