A parametric technique is presented for detection of Gaussian signals with unknown statistics in white Gaussian noise. The proposed method models the signal as an autoregressive process, and computes a test statistic (likelihood ratio) based on the limiting properties of the likelihood ratios used in the case of known statistics. Approximate distributions of the likelihood ratio are derived to predict the performance of the adaptive detection scheme. Some numerical examples are presented to validate the analysis.