Advanced fabrication techniques have now made it possible to produce microelectrode arrays for recording the electrical activity of a large number of neurons in the intact brain for both clinical and basic science applications. However, the long-term recording performance desired for these applications is hindered by a number of factors that lead to device failure or a poor signal-to-noise ratio (SNR). The goal of this study was to identify factors that can affect recording quality using theoretical analysis of intracortical microelectrode recordings of single-unit activity. Extracellular microelectrode recordings were simulated with a detailed multi-compartment cable model of a pyramidal neuron coupled to a finite-element volume conductor head model containing an implanted recording microelectrode. Recording noise sources were also incorporated into the overall modeling infrastructure. The analyses of this study would be very difficult to perform experimentally; however, our model-based approach enabled a systematic investigation of the effects of a large number of variables on recording quality. Our results demonstrate that recording amplitude and noise are relatively independent of microelectrode size, but instead are primarily affected by the selected recording bandwidth, impedance of the electrode-tissue interface and the density and firing rates of neurons surrounding the recording electrode. This study provides the theoretical groundwork that allows for the design of the microelectrode and recording electronics such that the SNR is maximized. Such advances could help enable the long-term functionality required for chronic neural recording applications.