Imaging systems have historically been associated with visual applications. Example applications include conventional photography, satellite imaging, and scientific visualization. Performance metrics relevant to these applications have traditionally been focused on visual quality and for such applications many conventional optical design metrics (e.g., worstcase spot size) can provide acceptable performance. Recent imaging applications however, have broken with this tradition and are concerned with information-sensitive performance metrics (e.g., bit-error-rate). Examples of such imaging systems include those used in page-oriented optical storage, optical interconnects, and various forms of task-specific imaging (e.g., target recognition). The ubiquitous nature ofdigital imaging modalities has made the need for fidelity-sensitive imaging system design even more apparent, with sensors and signal processing hardware k outpacing the perfonnance of low cost optical components. An information-optimized imaging system is one that takes as its primary goal the information throughput of the system. Notice that this goal can be very different from that of forming a "pretty picture." In this paper we limit our attention to binary-valued objects and within this framework we consider two cases. The first case will focus on diffraction-limited imaging systems whose performance is quantified via the average pixelwise mutual information. In section 2 we define the information-theoretic space-bandwidth-product (SBP) of such a system and demonstrate the existence of an optimal object configuration. The second case we consider is more realistic and includes the optical aberrations that characterize real world lenses. In this case we use the average bit-error-rate (BER) of the output image to quantify the overall imaging system performance. In section 3 we present the results of this case along with a comparison between some information-optimized designs and those based on more conventional design metrics.