The problem of combining task performance and routing for the detection of correlated random fields using multi-hop wireless sensor networks is considered. Under the assumption of Gauss-Markov structure along a given route, a link metric that captures the detection performance associated with a route is derived. Under Bayesian formulation Chernoff information is used as a performance criterion. It is shown that at high SNR Chernoff information is approximately given by a sum of the logarithm of the innovation variance at each link, which thus provides a link metric to determine the optimal route for the detection application. The value of the proposed metric is equivalent to the mutual information for Gaussian channel with signal power defined as the variance of signal innovation.