  Consider an ensemble of $N\times N$ non-Hermitian matrices in which all
entries are independent identically distributed complex random variables of
mean zero and absolute mean-square one. If the entry distributions also possess
bounded densities and finite $(4+\epsilon)$ moments, then Z. D. Bai [Ann.
Probab. 25 (1997) 494--529] has shown the ensemble to satisfy the circular law:
after scaling by a factor of $1/\sqrt{N}$ and letting $N\to \infty$, the
empirical measure of the eigenvalues converges weakly to the uniform measure on
the unit disk in the complex plane. In this note, we investigate fluctuations
from the circular law in a more restrictive class of non-Hermitian matrices for
which higher moments of the entries obey a growth condition. The main result is
a central limit theorem for linear statistics of type
$X_N(f)=\sum_{k=1}^Nf(\lambda_k)$ where $\lambda_1,\lambda_2,...,\lambda_N$
denote the ensemble eigenvalues and the test function $f$ is analytic on an
appropriate domain. The proof is inspired by Bai and Silverstein [Ann. Probab.
32 (2004) 533--605], where the analogous result for random sample covariance
matrices is established.
