  We study the convergence in distribution norms in the Central Limit Theorem
for non identical distributed random variables that is $$
\varepsilon_{n}(f):={\mathbb{E}}\Big(f\Big(\frac 1{\sqrt
n}\sum_{i=1}^{n}Z_{i}\Big)\Big)-{\mathbb{E}}\big(f(G)\big)\rightarrow 0 $$
where $Z_{i}$ are centred independent random variables and $G$ is a Gaussian
random variable. We also consider local developments (Edgeworth expansion).
This kind of results is well understood in the case of smooth test functions
$f$. If one deals with measurable and bounded test functions (convergence in
total variation distance), a well known theorem due to Prohorov shows that some
regularity condition for the law of the random variables $Z_{i}$, $i\in
{\mathbb{N}}$, on hand is needed. Essentially, one needs that the law of $
Z_{i}$ is locally lower bounded by the Lebesgue measure (Doeblin's condition).
This topic is also widely discussed in the literature. Our main contribution is
to discuss convergence in distribution norms, that is to replace the test
function $f$ by some derivative $\partial_{\alpha }f$ and to obtain upper
bounds for $\varepsilon_{n}(\partial_{\alpha }f)$ in terms of the infinite norm
of $f$. Some applications are also discussed: an invariance principle for the
occupation time for random walks, small balls estimates and expected value of
the number of roots of trigonometric polynomials with random coefficients.
