Non co-operative target identification using laser vibrometry is typically based upon characterization of the frequency spectra obtained after demodulating the vibrometer's output signal. The characterization uses information gleaned from certain identifiable features, such as tonals, which can be extracted from the vibration spectra. The success of this classification is dependent upon the performance of the demodulation scheme adopted. This paper investigates a number of different digital demodulation strategies that can be used on down-shifted and digitized vibrometer output in which the gross Doppler term has been removed. This paper presents an assessment of the likely impact of demodulation schemes upon several critical classification cues, for example signal-to-noise ratio, frequency and bandwidth. These cues help to parameterize the vibration spectrum and may in themselves be used to classify targets. Only digital schemes are considered here, in contrast to conventional vibrometer technology which uses analogue demodulation schemes. The investigations take the form of a general review, followed by a detailed description of each demodulation method. Each method is applied to a representative modulated signal, and its performance assessed qualitatively. Of key importance in this analysis are the different time-frequency representations (TFRs) of the digitized vibrometer signal, in addition to phase- differencing methods, which are used to derive the instantaneous frequency. TFRs which have been examined are the short-time Fourier Transform, Wigner and Choi-Williams distributions.