Speech recognition systems are ubiquitous and find its application in automated voice control, voice dialling and automated directory assistance. This paper aims at implementing a neural network based isolated spoken word recognition system on an embedded board-Raspberry Pi using open source software called octave. Mel-Frequency Cepstral Coefficient (MFCC) features are extracted from speech signal and given as input to the neural network. The Feed Forward Multi-Layer Perceptron Neural Network trained with back propagation rule is implemented using Octave in Raspberry Pi. TIDIGITS corpus is used for the experiment. Speaker dependent speech recognition results in 100% accuracy but the speaker independent recognition system shows less accuracy. © 2017 IEEE.