We study a spiked population model introduced in Johnstone (2001) where the population covariance matrix has all its eigenvalues equal to a constant value except for a few fixed eigenvalues (spikes). The classical factor models with random factors and homoscedastic errors is one particular instance of a spiked populaiton model. Determining the number of spikes is a fundamental problem which appears in many scienti c fi elds, including signal processing (linear mixture model) or economics (factor model). Several recent papers studied the asymptotic behavior of the eigenvalues of the sample covariance matrix when the dimension of the observations and the sample size both grow to innity so that their ratio converges to a positive constant. Using these results, we propose a new estimator of the number of spikes (or factors) based on the di fference between two consecutive sample eigenvalues.