Context : The observations carried out by the space missions CoRoT and Kepler provide a large set of asteroseismic data .
Their analysis requires an efficient procedure first to determine if a star reliably shows solar-like oscillations , second to measure the so-called large separation , third to estimate the asteroseismic information that can be retrieved from the Fourier spectrum .

Aims : In this paper we develop a procedure based on the autocorrelation of the seismic Fourier spectrum that is capable of providing measurements of the large and small frequency separations .
The performance of the autocorrelation method needs to be assessed and quantified .
We therefore searched for criteria able to predict the output that one can expect from the analysis by autocorrelation of a seismic time series .

Methods : First , the autocorrelation is properly scaled to take into account the contribution of white noise .
Then we use the null hypothesis H _ { 0 } test to assess the reliability of the autocorrelation analysis .
Calculations based on solar and CoRoT time series are performed to quantify the performance as a function of the amplitude of the autocorrelation signal .

Results : We obtain an empirical relation for the performance of the autocorrelation method .
We show that the precision of the method increases with the observation length , and with the mean seismic amplitude-to-background ratio of the pressure modes to the power 1.5 \pm 0.05 .
We propose an automated determination of the large separation , whose reliability is quantified by the H _ { 0 } test .
We apply this method to analyze red giants observed by CoRoT .
We estimate the expected performance for photometric time series of the Kepler mission .
We demonstrate that the method makes it possible to distinguish \ell = 0 from \ell = 1 modes .

Conclusions : The envelope autocorrelation function ( EACF ) has proven to be very powerful for the determination of the large separation in noisy asteroseismic data , since it enables us to quantify the precision of the performance of different measurements : mean large separation , variation of the large separation with frequency , small separation and degree identification .