An LMI-based technology is presented for signal recovery and identification from incomplete/noisy data available. It is based on minimizing the rank of a certain Hankel matrix related to the data, which is performed via the minimization of its nuclear norm. The latter problem is shown to be reconfigurable into a well-defined semi-definite program. The results of numerous test examples are presented; the drawbacks of the approach are discussed together with its generalizations.