The problem of extracting one out of a finite number of possible signals of known form given observations in an additive noise model is considered. Two approaches are studied: either the signal with shortest distance to the observed data or the signal having maximal correlation with some transformation of the observed data is chosen. With a weak signal approach, the limiting error probability is a monotone function of the Pitman efficacy and it is the same for both the distance-based and correlation-based detectors. Using the minimax theory of Huber, it is possible to derive robust choices of distancelcorrelation when the limiting error probability is used as performance criterion. This generalizes previous work in the area, from two signals to an arbitrary number of signals. We consider M-type and R-type distances and also one-dimensional as well as two-dimensional signals. Finally, some Monte Carlo simulations are performed to compare the finite sample size error probabilities with the asymptotic error probabilities.