Ferroresonance is a nonlinear phenomenon that could cause damage in the power systems equipment due to a high voltage and current during its sustained period. If the system is under sustained ferroresonance for a long time, it can cause thermal damage to the equipment. Therefore, it is important to eliminate or mitigate ferroresonance. It is shown that different mode of ferroresonance gives different impact to power systems. Thus, being able to detect and classify ferroresonance according to its mode during the transient period could avoid the system from going into the sustained period by initiating appropriate mitigation or elimination procedures. The objective of this research is to develop numerical algorithms for ferroresonance monitoring by analyzing its voltage and current signals using Fourier transform and wavelet transform. The aim of this research is to provide features that can be used in the development of a real time monitoring system that may be incorporated in mitigation or elimination procedures in the future. Ferroresonance voltage and current signals are obtained from the modelling of the ferroresonance circuit in the transient program. The sensitivity studies are performed to obtain different modes of ferroresonance and to observe the sensitivity of ferroresonance towards its initial condition and parameter variation. The signals are then being analyzed using Fourier transforms and wavelet transforms to obtain features that can be used in the classification process. Both the sustained and transient periods of the ferroresonance signals are analyzed. The results show that the ferroresonance voltage signals during sustained period are able to be classified according to their modes, however, the ferroresonance signals of the transient period requires further analysis. The algorithms are tested on the real data and produce the similar results that validate the algorithms.