In the nineties, two major events brought about a revolution in the field of digital communications: the invention of turbo codes and the development of multiple-input multiple-output (MIMO) wireless systems. The turbo codes are error-correcting codes which enable communication systems to operate close to the channel capacity with a reasonable complexity. Multiple-antenna transceivers, a.k.a. MIMO wireless systems, mitigate the effect of multipath fading that occurs in most terrestrial wireless communications. MIMO transmissions are more reliable than single-input single-output transmissions. Moreover, the data rate achievable by MIMO systems is also much higher. This thesis combines these two recent breakthroughs in digital communications. Iterative decoding is a key factor in the success of turbo codes. This principle has proved to be so powerful that it has soon been applied to other schemes such as iterative channel equalization, also known as turbo equalization. In the first part of this thesis, we derived a low-complexity iterative equalizer for frequency-selective MIMO channels. Its robustness against channel estimation errors was also addressed. The second part of this thesis is devoted to the estimation of the channel impulse response and the noise variance for coded transmissions over MIMO channels. We proposed several new iterative estimators based on the expectation-maximization algorithm, the expectation-conditionalmaximization algorithm and the minimum mean square error criterion. Finally, we derived lower bounds on the mean square error of channel estimators. In particular, the Cramer-Rao bound for the estimation of frequency-selective MIMO channels were computed. We mainly investigated the case where no training sequence are transmitted and the case where the receiver has a partial knowledge of the transmitted sequence.