Denoising Autoenoders with Variable Length Input
Recurrent Neural Networks can deal with variable length data. You might want to have a look at:


Andrej Karpathy: The Unreasonable Effectiveness of Recurrent Neural Networks.
Christopher Olah: Understanding LSTM Networks.
Hochreiter, Schmidthuber: Long short-term memory.


Another idea (which I have not tested so far and  just came to my mind) is using a histogram approach: You could probably make fixed-size windows, get the data from those windows and make it discrete (e.g. vector quantization, k-means). After that, you can make a histogram of how often those vectors appeared.

You could also use HMMs for recognition of variable length data.

Transformations (e.g. Fourier transform from the time domain in the frequency domain) might also come in handy.