An estimated 40,000 people have trauma related above knee amputations in the United States alone. A transfemoral prosthesis is an artificial limb that replaces the amputated limb. Although trauma related amputations are going down annually, there is still the need for prostheses that are capable of restoring normal biological knee function. Active micro-processor knees are a type of transfemoral prosthesis that can supply energy for activities therefore making many activities of daily living like chair and stair negotiation possible, how-ever these devices are not yet commercially available as it does not yet meet the requirement of robust and unambiguous mode switching. One aspect of active microprocessor knees that needs improvement is the intent recognition system that perceives the intent of the user. A novel hybrid intent recognition algorithm based on machine learning using solely mechanical signals was developed and tested in this thesis. The algorithm is capable of distinguishing be-tween the modes Standing, Sitting, Walking, Ramp Ascent, Ramp Descent, Stair Ascent and Stair Descent. The analysis of the algorithm was done on an open source healthy subject gait data set containing a total of 476 trials. The analysis involved determining recognition error rates and decision times for a novel subject’s data. The proposed algorithm fuses data from Inertial Measurement Units(IMUs) worn on the shank and knee joint encoders to make the decisions. The algorithm can achieve an overall error rate of 14.28%, the error rate reduces to 2.62% when grouping the Ramp Ascent and Ramp Descent together with the Walking mode.Decision times are, on average 9.59ms after a transition for critical transitions between stair modes and walking. For transitions between less critical modes like sitting and standing, decisions are taken with a maximum delay of 610ms. All transitions were successfully detected in 229 out of the 476 trials. The remaining trials had misclassifications due to improper labeling and variations in gait speed among the users. A preliminary analysis into adding ground reaction forces and moments indicates that the error rates can be decreased with it’s use. The research concludes that a hybrid classifier in which ramp walking is treated as level ground walking is a good starting point for implementing on the transfemoral prosthesis.