In this paper we apply model selection criteria in time series and regression analysis to the estimation of the number of signals in the MUSIC (Multiple Signal Classification) method. We compare the following model selection (information) criteria: AIC, HQ, BIC, AICC, and the recently introduced WIC as the weighted average of AICC and BIC. The general form of the above information criteria consists of a log likelihood function expressed in terms of the eigenvalues of the covariance matrix and a unique penalty term. In our estimation procedure, the number of signals is obtained by minimizing each of the above criteria. A linear antenna array example is presented to compare the performance of the above model selection criteria in signal processing problem.