Protein phosphorylation is a vital post-translational modification that is involved in a variety of biological processes. Several mass spectrometry-based methods have been developed for phosphoprotein characterization. In our previous work, we demonstrated the capability of a computational algorithm in mining phosphopeptide signals in large LC-MS data sets by measuring the mass shifts due to phosphatase treatment (Wu, H. Y.; Tseng, V. S.; Liao, P. C. J. Proteome Res. 2007, 6, 1812-1821). Mass accuracy seems to play an important role in efficiently selecting out phosphopeptide signals. In recent years, the hybrid linear ion trap (LTQ)/Orbitrap mass spectrometer, which provides a high mass accuracy, has emerged as a powerful instrument in proteomic analysis. Here, we developed a process to incorporate LC-MS data that was generated from an LTQ/Orbitrap mass spectrometer into our strategy for taking advantage of the accurate mass measurement. LTQ/Orbitrap raw files were converted to the open file format mzXML via the ReAdW.exe program. To find peaks that were contained in each mzXML file, an open-source computer program, msInspect, was utilized to locate isotopes and assemble those isotopes into peptides. An in-house program, LcmsFormatConverter, was utilized for signal filtering and format transformation. A proposed in-house program, DeltaFinder, was modified and used for defining signals with an exact mass shift due to the dephosphorylation reaction, which generated a table that listed potential phosphopeptide signals. The retention times and m/z values of these selected LC-MS signals were used to program subsequent LC-MS/MS experiments to get high-confidence phosphorylation site determination. Compared to our previous work finished by using a quadrupole/time-of-flight mass spectrometer, a larger number of phosphopeptides in the casein mixture were identified by using LTQ/Orbitrap data, demonstrating the merit of high mass accuracy in our strategy. In addition, the characterization of the lung cancer cell tyrosine phosphoproteome revealed that the use of alkaline phosphatase treatment combined with accurate mass measurement in this strategy increased data repeatability and confidence.