In this paper we present a hypothesis-verification approach for a Spanish Recognizer of continuously spelled names over the telephone. We give a detailed description of the spelling task for Spanish where the most confusable letter sets are described. We introduce a new HMM topology with contextual silences incorporated into the letter model to deal with pauses between letters, increasing the Letter Accuracy by 6.6 points compared with a single silence model approach. For the final configuration of the hypothesis step we obtain a Letter Accuracy of 88.1% and a Name Recognition Rate of 94.2% for a 1,000 names dictionary. In this configuration, we also use noise models for reducing letter insertions, and a Letter Graph to incorporate N-gram language models and to calculate the N-best letter sequences. In the verification step, we consider the M-Best candidates provided by the hypothesis step. We evaluate the whole system for different dictionaries, obtaining more than 90.0% Name Recogni...