Word spotting is a widely known subject in continuous speech recognition and the traditional approaches use either hidden Markov models (HMM) or Gaussian mixture models (GMM). In this paper, we propose a different approach based on language independent phoneme modeling. The proposed system is speaker and vocabulary independent, and it is easy to implement. An equal error rate (EER) of 3.34% and a figure of merit (FOM) of 45.58% are achieved on TIMIT corpus. Keywords: word spotting, continuous speech recognition, phonetic model, clustering, vocabulary independent, pattern matching