This paper presents our implementation techniques for a personalized semantic search engine. The system includes several components such as a crawler, a preprocessor, searcher and ranking module. PSSE uses multi-crawlers to traverse web to gather resources. The preprocessor is used to identify crawled page importance based on link analysis techniques, annotate resources using agents that also mine document content and determine term importance. In this process, natural language processing techniques (NLP) i.e. stop-word removing and word stemming are applied to the raw resources. Searcher is responsible in turn for query completion activities making use on ontology as well as maintaining a log from users’ search activities. Finally, the query engine delivers search results ranked based on a final score calculated based on traditional link analysis, content analysis and a weighted user profile. In this paper we evaluate an implementation of PSSE using traditional information retrieval performance measures namely, precision, recall and F-measure. Results of this implementation have shown that PSSE worked efficiently.