This letter presents a framework for the compressive acquisition of correlated signals. We propose an implementable sampling architecture for the acquisition of ensembles of correlated (lying in an a priori unknown subspace) signals at a sub-Nyquist rate. The sampling architecture acquires structured compressive samples of the signals after preprocessing them with easy-toimplement components. Quantitatively, we show that an ensemble of correlated signals each of which is bandlimited to and can be decomposed as the linear combination of underlying signals can be acquired at roughly (assuming without loss of generality ) samples per second. In the case, when , this results in significant reduction in the sampling rate compared to MW samples per second dictated by the Shannon-Nyquist sampling theorem. The signal reconstruction is achieved by solving only a least-squares program, which is in stark contrast to the prohibitively computationally expensive semidefinite programming techniques suggested in the earlier literature. We also provide rigorous analytical results to support our claims.