In many surveillance tasks it is very important for security operators to know whether a specific person is present in a given scene, at a given position and time. Person re-identification deals with this problem in order to provide more efficient security. A novel distributed appearance-based method for person re-identification is proposed. Spatio-temporal features are used to group the camera network into camera neighbourhoods. A intra-neighbourhood camera confidence hand-over measure is computed by exploiting a signatures' distance measure. The camera confidence measure is exploited to save network resources. Features that capture the chromatic appearance and the shape of an individual are used to compute a discriminative signature. The Expectation Maximization algorithm is used to fit Gaussian Mixture Models over the chromatic features. GMMs are exploited to compute the distance between signatures and to update the intra-neighbourhood camera confidence. The method has been validated using a benchmark dataset and a new dataset acquired from a wide camera network scenario.