A useful model in the analysis of multiple time series is to assume that each series is composed of a fixed signal common to all series and a wide sense stationary normal noise process. If the noise series are uncorrelated with identical autocorrelation functions, we have in the time domain a collection of independent normal vectors with a common covariance matrix. The likelihood ratio statistic for detecting the signal is then a singular version of Hotelling'sT2.