Cell formation is the process of dentifying independent part families and machine cells in a manufacturing system. Space search is an artificial intelligence technique for problem solving. In this thesis, an algorithm to perform space search for cell formation in the presence of alternative process plans has been developed. Simultaneous decision making for cell formation and process plan selection is performed by the algorithm. The algorithm starts by sorting machines in the manufacturing system according to SICGE coding and usage frequencies. Machines are arranged into the sorted list and the first one is selected as a key machine for a state. Parts with activities on the key machine are selected as candidate parts for family formation. Incremental part families are formed from the candidate parts and they become the root family of a child state. Space states are evaluated based on the count of the number of exceptional machine operations (operations outside the cell) in each state. Space states are explored based on the best search technique. The algorithm finds the best solution based on the number of exceptions. The algorithm can also handle multiple machine units assigning one machine unit to a cell if there is a demand for that machine in that cell. Two software prototypes called Factory Model Generator and Space Searcher were developed to perform collection of factory model information and space search on the cell formation problem. The algorithm has been tested on several examples from research journals for solution quality as well as to study the efficiency of the space search algorithm. Results obtained from the testing in terms of the number of exceptional machine operations show that algorithm performs better or on the same level as other previous algorithms.