Periodic signals hidden in noise may be detected by using correlation techniques This thesis presents a study of how correlation is approximated statistically, and carried out electronically. The ability to detect periodic signals in noise, using correlation, is justified mathematically in the first portion of the thesis. The discussion is based on the autocorrelation function and its characteristics. Considerable attention is given to showing the relationship between the time average of the autocorrelation function and its statistical average. This is necessary since statistical approximations are used in the actual correlator. It is possible to determine the frequency of the input signal, hidden in noise, by autocorrelation. The circuit design used to approximate the autocorrelation function is discussed in detail in the next section of the thesis. The system design uses all solid state components, including several field effect transistors. The last section of the thesis discusses the expected correlator output, compares this with the actual output, and explains any discrepancies between the two. Data is presented to verify a frequency response of 2kc - 30kc, and to show the quality of signal detection when the input N/s ratio is as high as 10db. The detection of a periodic pulse train in noise, and the possible application of this technique to communication line fault location is also presented.