We show that, given data from a mixture of $k$ well-separated spherical Gaussians in ${\mathbb R}^n$, a simple two-round variant of EM will, with high probability, learn the centers of the Gaussians to near-optimal precision, if the dimension is high ($n \gg \log k$). We relate this to previous theoretical and empirical work on the EM algorithm.