The FindEM particle picking program was tested on the publicly available keyhole limpet hemocyanin (KLH) dataset, and the results were submitted for the "bakeoff" contest at the recent particle picking workshop (Zhu et al., 2003b). Two alternative ways of using the program are demonstrated and the results are compared. The first of these approximates exhaustive projection matching with a full set of expected views, which need to be known. This could correspond to the task of extending a known structure to higher resolution, for which many 1000's of additional images are required. The second procedure illustrates use of multivariate statistical analysis (MSA) to filter a preliminary set of candidate particles containing a high proportion of false particles. This set was generated using the FindEM program to search with one template that crudely represents the expected views. Classification of the resultant set of candidate particles then allows the desired classes to be selected while the rest can be ignored. This approach requires no prior information of the structure and is suitable for the initial investigation of an unknown structure--the class averages indicate the symmetry and oligomeric state of the particles. Potential improvements in speed and accuracy are discussed.