Tremor is defined as the involuntary rhythmic or quasi-rhythmic oscillation of a body part, resulting from alternating or simultaneous contractions of antagonistic muscle groups. While tremor may be physiological, those who have disabling pathological tremors find that performing typical activities for daily living to be physically challenging and emotionally draining. Detecting the presence of tremor and its proper identification are crucial in prescribing the appropriate therapy to lessen its deleterious physical, emotional, psychological, and social impact. While diagnosis relies heavily on clinical evaluation, pattern analysis of surface electromyogram (sEMG) signals can be a useful diagnostic aid for an objective identification of tremor types. Using sEMG system attached to several parts of the patient's body while performing several tasks, this research aims to develop a classifier system that automates the process of tremor types recognition. Finding the optimal model and its corresponding parameters is not a straightforward process. The resulting workflow, however, provides valuable information in understanding the interplay and impact of the different features and their parameters to the behavior and performance of the classifier system. The resulting model analysis helps identify the necessary locations for the placement of sEMG electrodes and relevant features that have significant impact in the process of classification. These information can help clinicians in streamlining the process of diagnosis without sacrificing its accuracy.