This paper considers piecewise affine models of genetic regulatory networks and focuses on the problem of detecting switches among different modes of operation in gene expression data. This task constitutes the first step of a procedure for the complete identification of the network and complements the algorithms proposed in (Drulhe et al., 2006). We propose two methods for switch detection. The first one is based on the computation of suitable indexes that emphasize the occurrence of switches in the data. The second one exploits nonlinear identification techniques in order to recast switch detection into an hypothesis testing problem. In both cases we assume that the expression of individual genes obeys to an output-error piecewise affine dynamics and we study the performance of the proposed algorithms for different noise levels. We also illustrate the application of our methods to the reconstruction of switching times in data produced by a piecewise affine model of the network regulating the carbon starvation response in Escherichia coli.