Is it possible to design a band-pass filter with fewer taps than a series HPF + LPF? I am digitally filtering a signal that is streaming live. The goal is to reduce the filter delay as much as possible while maintaining the quality of some measurements based on the filtered signal. For legacy reasons, the system was built with a low-pass and high-pass FIR filter in series - each filter has the same number of taps (kernel size), $n$. 

I realized though, we are no longer constrained to these and could implement a single FIR band-pass filter. If I can reduce number of filter taps involved from $2n$ that will reduce the delay. My question is: is it possible to design this band-pass filter with fewer taps (a smaller kernel) than the combined taps (kernels) of the LPF/HPFs while achieving the same performance?

My initial thought is FIR filtering is a convolution operation, and thus linear + associative. That means you can construct the band pass kernel by convolving the LPF + HPF kernels. I'm unsure how to interpret this in terms of taps/kernel size because the convolution in some sense assumes an infinite kernel.

Thanks!
Assuming that your legacy FIR linear phase filters were designed using windowing method, then yes, you can combine those two FIR linear phase filters into a single FIR linear phase filter and it will be shorter than the the serial cascade. 

However, you won't be convolving the individual impulse responses to get it. Rather, you should use a multi-band windowed FIR filter design technique. An example of how to do it can be found on Alan Oppenheim's DTSP book on chapter 7 filter design section. 

Specifically he makes use of Kaiser window design approach acording to which the filter (window) length is empricially dependent on the transition band width and peak approximation error. 

So, given the same transition band width and error ripples of a HPF -> LPF cacade, whose lengths are M samples each, you would be using a single multiband filter of length M with the same transition band and error ripple.

Note that as MattL also indicates, you may also chose different filter types to achieve similar respones, but that depends on what you want to achieve finally.