Accidental falls are a major health concern among older adults. Currently, fall prevention programs employ clinical assessment scores for identifying elderly fallers based on cut-off values. Biomechanical parameters provide crucial information differentiating pathological gait and posture and can be used to classify elderly fallers and non-fallers. Pattern recognition models based on biomechanical parameters may provide greater insight for such classification. The purpose of this study was to compare the classification accuracy of different pattern recognition models for identifying elderly fallers using biomechanical parameters measured during balance and gait tasks. Pattern recognition models were also developed using clinical assessment scores and compared to the models based on biomechanical parameters for accurately identifying elderly fallers. Participants included 58 non-fallers (age = 72.3 ± 5.7) and 41 fallers (age = 74.0 ± 12.3) who performed balance and gait tasks on a walkway with embedded force plates and pressure mats. The parameters included 2D ground reaction force (GRF), center of pressure (COP), and the plantar pressure (PP). Using this data as input, different classification algorithms were used to build models. Maximum accuracy of 86.02% for classifying faller/non-faller categories was obtained using a classifier based on biomechanical parameters from combined gait and balance tasks. The GRF parameters ranked higher than COP and PP parameters based on F-score ranking suggesting predictor importance of GRF parameters. The classification performance was further improved by adding GRF parameters to the more commonly used COP parameters. However, the classifiers based on clinical assessment scores resulted in a maximum accuracy of 92.93% suggesting that elderly fallers can be accurately classified using pattern recognition models based on clinical assessment scores.