As extensive mass spectrometry-based mapping of the phosphoproteome progresses, computational analysis of phosphorylation-dependent signaling becomes increasingly important. The linear sequence motifs that surround phosphorylated residues have successfully been used to characterize kinase-substrate specificity. Here, we briefly describe the available resources for predicting kinase-specific phosphorylation from sequence properties. We address the strengths and weaknesses of these resources, which are based on methods ranging from simple consensus patterns to more advanced machine-learning algorithms. Furthermore, a protocol for the use of the artificial neural network based predictors, NetPhos and NetPhosK, is provided. Finally, we point to possible developments with the intention of providing the community with improved and additional phosphorylation predictors for large-scale modeling of cellular signaling networks.