We present a method that estimates the strength of neuronal oscillations at the cellular level, relying on auto-correlation histograms computed on spike trains. The method delivers a number, termed oscillation score, that estimates the degree to which a neuron is oscillating in a given frequency band. Moreover, it can also reliably identify the oscillation frequency and strength in the given band, independently of the oscillation in other frequency bands, and thus it can handle superimposed oscillations on multiple scales (theta, alpha, beta, gamma etc). The method is relatively simple and fast. It can cope with a low number of spikes, converging exponentially-fast with the number of spikes, to a stable estimation of the oscillation strength. Hence, it lends itself to the analysis of spike-sorted single unit activity from electrophysiological recordings. We show that the method performs well on experimental data recorded from cat visual cortex and also compares favorably to other methods. In addition, we provide a measure, named confidence score, that determines the stability of the oscillation score estimate over trials.