Say we have a vector of $k$ random variables $$m\sim N(0,\Sigma)$$ Then it is the case that $m^T\Sigma^{-1}m$ has a $\chi^2_k$-distribution, because it is the sum of $k$ independent standard normal variables.
Now, if $\Sigma$ is a diagonal matrix, so that the $m_i$ are independent, then I understand why this is the case. We simply get: $$m^T\Sigma^{-1}m=\frac {m_1^2}{\sigma_1^2}+...+\frac {m_k^2}{\sigma_k^2}$$
However, when the variables are correlated, then I can't prove the result. How do we show the general case?