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This paper examines the Cramer-Rao (CR) lower bound on the variance of frequency estimates for the problem of n signals closely spaced in frequency. The main results presented are simple analytic expressions for the CR bound in terms of the maximum frequency separation, δω, SNR, and the number of data vectors, N, that are valid for small δω. The results are applicable to the conditional (deterministic) signal model. The results show that the CR bound on frequency estimates is proportional to (δω)-2(n-1)/N×SNR. Therefore, the bound increases rapidly as the signal separation is reduced. Examples indicate that the expressions closely approximate the exact CR bounds whenever the signal separation is smaller than one resolution cell. Based upon the results, it is argued that the threshold SNR at which an unbiased estimator can resolve n closely spaced signals is at least proportional to (δω)-2n/N. The results are quite general and apply to many different types of temporal and spatial sampling grids