In the present work, an Auto Regressive Moving Average (ARMA) model and a Discrete Wavelet Transform (DWT) are applied on vibrational signals, acquired by an accelerometer placed on the cylinder block of a Spark Ignition (SI) engine, for knock detection purposes. To the aim of tuning such procedures, the same analysis has been carried out by using the traditional MAPO (Maximum Amplitude of Pressure Oscillations) index and an Inverse Kinetic Model (IKM), both applied on the in-cylinder pressure signals. Vibrational and in-cylinder pressure signals have been collected on a four cylinder, four stroke engine, for different engine speeds, load conditions and spark advances. The results of the two vibrational based methods are compared and in depth discussed to the aim of highlighting the pros and cons of each methodology. The presented outcomes show the capability of the vibration based detection algorithms in accurately monitor the presence of knock phenomena, and to determine its intensity according to the IKM and MAPO based methods. The comparison with a standard processing of the knock sensor output reveals the higher sensitivity of the proposed methodologies. Therefore, the possibility of implementation in modern on-board control units is foreseen, as well.